{"as_of":"2026-08-14T21:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:24eb0e6f698cdd98cd0b5efa1f9ab670f13b390c77cfdeb90b600fc9f4603d8b","coverage":[{"denominator":193,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:35:25.973511Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.18401/citation-record","integrity":"/paper/2505.18401/integrity","json":"/paper/2505.18401/citation-record.json","paper":"/paper/2505.18401"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.566037Z","title":"Social lstm: Human trajectory prediction in crowded spaces","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.566037Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:384e7cef914901a79fd57af1fd1ff1a4f6c56a2c4d562b25134a282526e741fe","observation_id":"5d0fee5c-3457-44b4-8215-8b90bd820471","resolution":{"observed_at":"2026-08-07T14:35:25.566037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.570883Z","title":"Social ways: Learning multi-modal distributions of pedestrian trajectories with gans","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.570883Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:58951761a7116ce7091c0de0bcd081df605cb2258fa48dd2bea6bed2ef250d00","observation_id":"aae4858f-e619-41b7-98a3-eb2052b5a118","resolution":{"observed_at":"2026-08-07T14:35:25.570883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.575089Z","title":"Context-aware trajectory prediction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.575089Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:b4cddede5b91341387419650e9a3f6e47b2aeac25b13b6b755da812981d5e7e8","observation_id":"594cc9ca-56a7-4bd4-88f8-b8327f85cce3","resolution":{"observed_at":"2026-08-07T14:35:25.575089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.579803Z","title":"Crowd characterization in surveillance videos using deep-graph convolutional neural network","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.579803Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:2f73ee4b5c33cc4c6866e01b941d0645f9e37bc6ed9c4fe93e958cc1e2e9b884","observation_id":"9238ed61-e26c-498f-921b-7003bc33fdcd","resolution":{"observed_at":"2026-08-07T14:35:25.579803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.584786Z","title":"Understanding crowd flow patterns using active-langevin model","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.584786Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:e385d53adc4b128ba118494084bbc49de9160f68423147c1b84cca4d83a0bf13","observation_id":"c617f309-6eee-4f6d-b742-ca3b2739398b","resolution":{"observed_at":"2026-08-07T14:35:25.584786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.589180Z","title":"Characterization of orderly behavior of human crowd in videos using deep learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.589180Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:0a75c21073c6d0742b47782ab40490da67eff3632e7eecc0f63301ad8fdc496c","observation_id":"7e085cdc-98a2-4ae1-aacc-035a2176a256","resolution":{"observed_at":"2026-08-07T14:35:25.589180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.594308Z","title":"Pidlnet: A physics-induced deep learning network for characterization of crowd videos","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.594308Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:2455fc3295a21acef8a3b74d2442618d394997964a219fd81c119ee665952089","observation_id":"77466ca5-05dc-40f1-a721-d8635d63950d","resolution":{"observed_at":"2026-08-07T14:35:25.594308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.598419Z","title":"Ensemble classification of video-recorded crowd movements","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.598419Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:76197c49f4d46165b36760b490152f9d03bd3e7abf5bb12519af78680cb1989b","observation_id":"7e16e5e2-e939-43da-b911-e2f81a677629","resolution":{"observed_at":"2026-08-07T14:35:25.598419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.602680Z","title":"Group lstm: Group trajectory prediction in crowded scenarios","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.602680Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:d81ede9988aa7eecff596b053a5e489d3585759a26a354a517344ff00e09e4eb","observation_id":"fbd6d0b2-1f40-4480-9755-46da01cc8883","resolution":{"observed_at":"2026-08-07T14:35:25.602680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.607151Z","title":"Embedding group and obstacle information in lstm networks for human trajectory prediction in crowded scenes","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.607151Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:c79bcbacea8f7eeaed576afc075f4f9586d8142e4924b9e4caf2a7a289a0aaf9","observation_id":"272dde20-a404-486c-92e8-2e5a3610d6d8","resolution":{"observed_at":"2026-08-07T14:35:25.607151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.611064Z","title":"An introduction to the kalman filter","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.611064Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:804b5c246dd77db9545987f98aa338216bfad28ebbcfaa80abdbed2bbacf5630","observation_id":"ce2494d6-015a-4f97-8f92-3c64534b856e","resolution":{"observed_at":"2026-08-07T14:35:25.611064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.615022Z","title":"Latent dirichlet allocation","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.615022Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:4363f89a62ae1eb8cc3e4299007cbbefdb2a7228d1f7eaf0d643794801a578d7","observation_id":"039d1764-6caf-4dcc-90b6-896308369ebe","resolution":{"observed_at":"2026-08-07T14:35:25.615022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.619135Z","title":"A short review of deep learning methods for understanding group and crowd activities","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.619135Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:a12f5083a9fb8c7ffdd638c1640b49cc502eacf5e2aa6a8b2a2905fc607f3598","observation_id":"2b9d12c5-fe81-4e8e-bb36-91d8cda5b7ab","resolution":{"observed_at":"2026-08-07T14:35:25.619135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.623344Z","title":"Pedestrian models for autonomous driving part ii: high-level models of human behavior","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.623344Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:d1510a4b89120058e4748c080450f0db85f069ad1e61078041de14667196475e","observation_id":"d0fdf595-7e82-4244-a53e-e1fdcc3f00c6","resolution":{"observed_at":"2026-08-07T14:35:25.623344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.627473Z","title":"Quo vadis, action recognition? a new model and the kinetics dataset","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.627473Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:a763105c8bf57d1392d628467b3326356a9603fd76ea46ca0b3880a24fbb0eb6","observation_id":"9232588a-735c-429c-837e-450395eb171c","resolution":{"observed_at":"2026-08-07T14:35:25.627473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.631585Z","title":"Fight detection with spatial and channel wise attention-based convlstm model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.631585Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:87bc6091d76c2b9b913fda36d12588f26a217fa7ba83f4b16b432a344afb51ee","observation_id":"1d8f5083-7afb-4859-a107-9025c98ab65a","resolution":{"observed_at":"2026-08-07T14:35:25.631585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.635967Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.635967Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:b9c271061da981ab8b7b5050ad46625f0715e38d642dabaa7f05fca77635ac8e","observation_id":"3c2c332b-0ba0-4e25-be4e-c8580026e794","resolution":{"observed_at":"2026-08-07T14:35:25.635967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.640313Z","title":"Neural ordinary differential equations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.640313Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:09e7d07d1d53ea13f6e5263ef917b0b7511289c3d76f6f17c4a2501e054c5e85","observation_id":"9d1190e1-3eda-4875-9be8-b2929e2829e1","resolution":{"observed_at":"2026-08-07T14:35:25.640313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.644386Z","title":"Multimodal pedestrian trajectory prediction using probabilistic proposal network","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.644386Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:e472fec1dc2841257ac0926ff2ad0c0e7dd4af78801ae19d37fe83092aabc536","observation_id":"199336b8-0fa6-434f-ae3f-bf4b345e4e8a","resolution":{"observed_at":"2026-08-07T14:35:25.644386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.648251Z","title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.648251Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:796157b2d3038ff8050194b80c793ec2b02d1f54f754b1c631400c2a5f5b77f8","observation_id":"16c9cc78-42f1-4000-b153-a9ae9b9a48cc","resolution":{"observed_at":"2026-08-07T14:35:25.648251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.652492Z","title":"Three-dimensional atrous inception module for crowd behavior classification","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.652492Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:d28b152b14e10b5c7a47960d7dfb4c2d0fa443804c0d905d1f755846a8708fc7","observation_id":"ee74a36c-68a9-4a3d-b2d3-3bee4a045c42","resolution":{"observed_at":"2026-08-07T14:35:25.652492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.656276Z","title":"A unified framework for multi-target tracking and collective activity recognition","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.656276Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:7100ab4a83af5368f2dedb185fc58c3e18a4c9cbbb0046a140187bf7cfd0b0b1","observation_id":"10e7a034-75bb-4860-ad8f-b96abd5a12d6","resolution":{"observed_at":"2026-08-07T14:35:25.656276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.660268Z","title":"What are they doing?: Collective activity classification using spatio-temporal relationship among people","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.660268Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:3141e16fb02f65f88fa1bda4fcdf033ff4316edd6873a2c556238e0e29e9dcd0","observation_id":"f5326511-1058-41fa-8b02-f31d71943730","resolution":{"observed_at":"2026-08-07T14:35:25.660268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.663988Z","title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.663988Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:a7c53883b2b9ee146165a39c1035c66922d73fcb97e0b17676f029be77a9a82c","observation_id":"02dbb06e-f493-4186-bd60-41a3e53ffb75","resolution":{"observed_at":"2026-08-07T14:35:25.663988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.668198Z","title":"A recurrent latent variable model for sequential data","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.668198Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:025ac2b56fdd03480ed0688c14b92c1848525b64f76189b2be667f647ecfd272","observation_id":"9bf94cbf-668c-446f-b340-3f7146f5d084","resolution":{"observed_at":"2026-08-07T14:35:25.668198Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.672161Z","title":"Social-vrnn: One-shot multi-modal trajectory prediction for interacting pedestrians","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.672161Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:9cb0a80c89cef67652e58b2885c7fa6f0dc2c37b12477b3f79a056d31ae067d0","observation_id":"a5922077-1bde-4f61-b656-4f730726c3a9","resolution":{"observed_at":"2026-08-07T14:35:25.672161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.676157Z","title":"Behavior recognition based on category subspace in crowded videos","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.676157Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:daa5917e8ffcb50dd05af914a018d30272e7df303cf6eb2a9cb110a372618316","observation_id":"94f7ec9c-7260-4ddb-a711-b61373a96260","resolution":{"observed_at":"2026-08-07T14:35:25.676157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T14:35:25.680257Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.680257Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:d9b5c6bb670ae523fec706bf61150d554ee6573da5cabee5b3dc22f986c2bdbd","observation_id":"95675980-d290-426f-980f-344525a2fed7","resolution":{"observed_at":"2026-08-07T14:35:25.680257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.685038Z","title":"Modelling pedestrian trajectory patterns with gaussian processes","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.685038Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:c054554a9475ce6369a755f96180bffb6f28649f3c7bf55c1f50596a2070b03d","observation_id":"cbd10707-03f5-4e69-9e9d-db68617a5e37","resolution":{"observed_at":"2026-08-07T14:35:25.685038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.689710Z","title":"The ensemble kalman filter: Theoretical formulation and practical implementation","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.689710Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:73d24fd738971f29bbe77923086e8b288e1d6d1a6e0c7a6bf7dc492607f09fd9","observation_id":"4e94aa21-683b-4dd9-8020-6d275d1bc27d","resolution":{"observed_at":"2026-08-07T14:35:25.689710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.694003Z","title":"Citymomentum: an online approach for crowd behavior prediction at a citywide level","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.694003Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:3d001f983a3be8dff120af00b1efc87388d6e47554d66aa871b0ed56ace015b5","observation_id":"cfc2217c-5da4-4526-aa93-b091cf6d9731","resolution":{"observed_at":"2026-08-07T14:35:25.694003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.698083Z","title":"Fundamentals of neural networks: architectures, algorithms, and applications, 1994","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.698083Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:0127c78efb048b172630f2fde015c8f3726f1d736e025f89c98bb4bdbfd47172","observation_id":"1d2892c4-4440-4712-8968-180d1125e13c","resolution":{"observed_at":"2026-08-07T14:35:25.698083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.702043Z","title":"Forward propagation of a push through a row of people","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.702043Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:af17b13de740f12c3447dd3ed124b9ecf392d49b3dd6ecada2c6af0e072ba728","observation_id":"b396d56d-5160-40a8-a638-395314fac9c9","resolution":{"observed_at":"2026-08-07T14:35:25.702043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.707163Z","title":"Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.707163Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:b27a4ff46ccb9fee12f6f805bffee0916b57bfbf30a1a0c0ab6f652084566250","observation_id":"732c09e4-787a-439b-aa88-84ce46c67a91","resolution":{"observed_at":"2026-08-07T14:35:25.707163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.710826Z","title":"Markov models and hidden M arkov models: A brief tutorial","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.710826Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:6ac81ed272f71417b1a3040e7dcc088598a8d7fdb76cce233255738f26c71f72","observation_id":"d8476b9b-618f-4957-b2c3-421762a3c83f","resolution":{"observed_at":"2026-08-07T14:35:25.710826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.714906Z","title":"Spatio-temporal attention transformer model for future trajectory forecast","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.714906Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:c5449cfa3e2a294d79f5fdc4bff54e3b5990566f4071f356f13ba2499f2e3cf8","observation_id":"15c1235b-38f8-4010-a92a-9f17c28ba69e","resolution":{"observed_at":"2026-08-07T14:35:25.714906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.718834Z","title":"Transformer networks for trajectory forecasting","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.718834Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:515a7115cc15e5a9c3159da397f4bff7097b982a67af05e30d1a9d50ec57c2e4","observation_id":"de656f03-a216-4bb8-a68a-2461fe8be88c","resolution":{"observed_at":"2026-08-07T14:35:25.718834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.722948Z","title":"Continuum modeling of crowd turbulence","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.722948Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:038fb0cde194a02b3b12040dc021b9d6ee081cdd12acf3297c50d0540421613d","observation_id":"405d4f77-799d-4618-8cd2-c4f72c0185cd","resolution":{"observed_at":"2026-08-07T14:35:25.722948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.726715Z","title":"Resolving collisions in dense 3d crowd animations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.726715Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:0367185372a2edd9114239dcb83f7d5eac02703b37d74157563cc1b19a93cfda","observation_id":"fd725c8d-5dae-49bc-94eb-a3f9264b81e0","resolution":{"observed_at":"2026-08-07T14:35:25.726715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.730329Z","title":"Generative adversarial nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.730329Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:fb4ea857da30b277bddeed364b90134cd269acc4d835c302ac33036bbb7699be","observation_id":"d4651404-021e-4dc1-a96e-06424fcbab6a","resolution":{"observed_at":"2026-08-07T14:35:25.730329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.734677Z","title":"Stochastic trajectory prediction via motion indeterminacy diffusion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.734677Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:c7b42759c702f6ebdae272941cd3c06245f4fe1615fb74cf5809f94179aa1e76","observation_id":"d1285cc4-7747-4b1b-871c-cecf26436d63","resolution":{"observed_at":"2026-08-07T14:35:25.734677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.738848Z","title":"Social gan: Socially acceptable trajectories with generative adversarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.738848Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:19a78fbaa998ff436755e36a47d462abfb68eb308a864dddc1f4347bf97c8283","observation_id":"c48a8cec-ca34-4bd7-a453-1f4f0e391818","resolution":{"observed_at":"2026-08-07T14:35:25.738848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6145.23662","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:26.713440Z","title":"Guy, Jur van den Berg, Wenxi Liu, Rynson Lau, Ming C","venue":null,"work_id":"f0f73111-8292-41b7-8103-bb659d1927de","year":2012},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.742608Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:387cc21af0feead1a41a7e29ce4f093a7c4514e1548cf9c9a3c8c24d6291624c","observation_id":"4fceb606-b8c8-4a8d-a32f-2e2de59b5204","resolution":{"observed_at":"2026-08-07T14:35:26.722012Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.12556","last_updated":"2023-07-10T13:54:04Z","snapshot_observed_at":"2026-08-05T14:26:27.363682Z","submitted_at":"2020-12-23T09:37:54Z","title":"A Survey on Visual Transformer","version":6},"cited_work":{"arxiv_id":"2012.12556","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.12556","snapshot_observed_at":"2026-08-07T14:35:26.639115Z","title":"A Survey on Visual Transformer","venue":"cs.CV","work_id":"f467ae70-d2e7-44bb-b68a-ad3219f40508","year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.746831Z"},"links":{"cited_paper":"/paper/2012.12556","citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:0ee3512d786c157f7c5283d9a3a1a82e74381e1f68655bad9ce6b0d00fda057a","observation_id":"1a68db03-7e90-4d1a-93fd-6d21ec572813","resolution":{"observed_at":"2026-08-07T14:35:26.643888Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.751424Z","title":"Learning spatio-temporal features with 3d residual networks for action recognition","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.751424Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:19ef66a8dbf0627c7ba07b3e7194d52094f80bdc812e35432f143c833bf4a621","observation_id":"056aad40-30ba-4dbb-b0df-7eab384a81e3","resolution":{"observed_at":"2026-08-07T14:35:25.751424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.755517Z","title":"Informative scene decomposition for crowd analysis, comparison and simulation guidance","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.755517Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:12cc51de2d784495f523da99a90374e2f542194b54b26fef45015071a54de284","observation_id":"d8ccdc0c-917a-4111-9ec9-c735f37c3cc5","resolution":{"observed_at":"2026-08-07T14:35:25.755517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12168","last_updated":"2025-03-15T15:14:26Z","snapshot_observed_at":"2026-08-07T17:01:49.010832Z","submitted_at":"2025-03-15T15:14:26Z","title":"Learning Extremely High Density Crowds as Active Matters","version":1},"cited_work":{"arxiv_id":"2503.12168","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.12168","snapshot_observed_at":"2026-08-07T14:35:26.618135Z","title":"Learning Extremely High Density Crowds as Active Matters","venue":"cs.CV","work_id":"de64d6f7-5c55-4741-b423-039c82ab4048","year":2025},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.759421Z"},"links":{"cited_paper":"/paper/2503.12168","citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:509599df1a5e7e594940638fbd0c1c5054a52f9140aaff192a3a7936cf8816fd","observation_id":"ee866f90-9f89-49d2-8fe8-34d05ad70c2f","resolution":{"observed_at":"2026-08-07T14:35:26.622793Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.763833Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.763833Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:84bb9e61d59d51d23c951afd7f3c0b40d71cc2ace81ac14ba6c270d677a667ef","observation_id":"2aca5d50-3a49-4db4-a909-6149e7a38b0a","resolution":{"observed_at":"2026-08-07T14:35:25.763833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.767600Z","title":"Social force model for pedestrian dynamics","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.767600Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:d25e46c05070073f6b8e4f1bec93b41973090afad6d73843e1da7c0985812207","observation_id":"0255fbce-5ae1-4947-9201-e1ee68382453","resolution":{"observed_at":"2026-08-07T14:35:25.767600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.771690Z","title":"Long short-term memory","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.771690Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:30a667c9ef33b016f2acbb2809624b56144b1363519c35a1ae3ff57291d6a27c","observation_id":"0944b726-21f1-4b1e-b802-b8cc0fc829aa","resolution":{"observed_at":"2026-08-07T14:35:25.771690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.775432Z","title":"Approximation capabilities of multilayer feedforward networks","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.775432Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:9bd6d26a97a3ecf13e6e3bf27cc4f8a8fa3dfb31391199f80a688e383baa7106","observation_id":"dcb75dd0-05e9-48fb-b42b-fc5b25bdf03f","resolution":{"observed_at":"2026-08-07T14:35:25.775432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.779616Z","title":"Multilayer feedforward networks are universal approximators","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.779616Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:20ed73e2b6ae3ccb52504f09c782f3a9155c563df1622cbe5ecfb3f56332ac57","observation_id":"4fc0a8ef-5d42-44a1-aaa0-3b3275855b45","resolution":{"observed_at":"2026-08-07T14:35:25.779616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.783330Z","title":"Stgat: Modeling spatial-temporal interactions for human trajectory prediction","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.783330Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:f040daed12329688cbfe3fd86a01be71cb7139547db4df22b4c297238c6a4016","observation_id":"c6167387-2fa9-4113-ba46-f1b9d6a1741a","resolution":{"observed_at":"2026-08-07T14:35:25.783330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.787298Z","title":"A continuum theory for the flow of pedestrians","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.787298Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:ce76f55cf74c53bb21c7bbc564c94a4f3cbaab099b04dae12fb87f2d6e3ac5d2","observation_id":"62e80fb9-7f29-498a-b2b6-99d1df18daa7","resolution":{"observed_at":"2026-08-07T14:35:25.787298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.791622Z","title":"Interpretable self-aware neural networks for robust trajectory prediction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.791622Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:ac9aaa84e7f7dd71ee06e2706d9fd92b5e4e9c7db3616b15826eb12b36365567","observation_id":"85d7ca3e-2afb-4d1c-a01f-5e85d47bf287","resolution":{"observed_at":"2026-08-07T14:35:25.791622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.795391Z","title":"Discrete residual flow for probabilistic pedestrian behavior prediction","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.795391Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:e0663fd5be1dc5e020abb3d75d076742d6979c523d1dc97711910f2428c2fc99","observation_id":"3e548e5a-5a32-4bd0-b070-a34eacf3b680","resolution":{"observed_at":"2026-08-07T14:35:25.795391Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.799070Z","title":"The material point method for simulating continuum materials","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.799070Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:487f4961a9f50554531c7902c3ce53a32e46183be83079244284de1fc7ab636e","observation_id":"7370236e-dd53-4c6a-a889-9597d070eb2e","resolution":{"observed_at":"2026-08-07T14:35:25.799070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.803616Z","title":"Deepcrowd: A deep model for large-scale citywide crowd density and flow prediction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.803616Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:8ab24fab4cd9f1c2f8becad22ebad88aae1a424fccaa9f135708c4a771ea69b2","observation_id":"97c9472e-dee9-4932-8ec5-baf4533ae98e","resolution":{"observed_at":"2026-08-07T14:35:25.803616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.807968Z","title":"Crowd behavior recognition using dense trajectories","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.807968Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:2fa50965f4a49bfe585c4606ee1e7be5a08f42a6df488a125dafc029b04bdcef","observation_id":"701e6611-eb65-4d75-8cbf-7cf1b205ce2a","resolution":{"observed_at":"2026-08-07T14:35:25.807968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.812227Z","title":"On neural differential equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.812227Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:61d691f12b92de739657b8c254bb30fff53f16e08c69a45cdba2267f82208788","observation_id":"f9200720-592c-4b09-9d06-50b0421f1e45","resolution":{"observed_at":"2026-08-07T14:35:25.812227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.816019Z","title":"Brvo: Predicting pedestrian trajectories using velocity-space reasoning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.816019Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:f4c57d1841ef563825d06592d1033ee9c9368cc7ae169b652b0e0dc3bb945284","observation_id":"14fcc052-ce26-4a5d-8efa-7141bc75690a","resolution":{"observed_at":"2026-08-07T14:35:25.816019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-07T14:35:25.820026Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.820026Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:062b8d7eddf9a397fd2bd564bb749ac0fec635ab6e24d3966bfb079fb33de51a","observation_id":"8c5bf643-e4d1-4c50-8019-d7ebb1be08f2","resolution":{"observed_at":"2026-08-07T14:35:25.820026Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-07T14:35:25.824151Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.824151Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:479b0178ae9b088568bb3d5b2aec3a6de53687eeedf60ede90fcb17f14161b13","observation_id":"3e162658-a2fb-4e93-b222-7b837e35237f","resolution":{"observed_at":"2026-08-07T14:35:25.824151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.828580Z","title":"Activity forecasting","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.828580Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:cf75d2a6d6eea27e274cbf6d27ba7a2e3df042e3d9f1e76f3b764855c4df0627","observation_id":"dcd424b8-5705-477f-a712-9376e42eced3","resolution":{"observed_at":"2026-08-07T14:35:25.828580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.832493Z","title":"Crowd behavior analysis: A review where physics meets biology","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.832493Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:a9e9c42b3699a63b8a280bf5c1a4c29f385d8337c04505b61ec46c8649990437","observation_id":"e1619c0c-e134-4a0a-a008-b2c3107bc133","resolution":{"observed_at":"2026-08-07T14:35:25.832493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.836901Z","title":"Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.836901Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:c2602f5017b733de0816c87808499e41387286d8cfaec44c7bc757d2e092f5fd","observation_id":"7f9e5da5-be8a-4362-ac39-4e5b3d436f5e","resolution":{"observed_at":"2026-08-07T14:35:25.836901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.841429Z","title":"Handwritten digit recognition with a back-propagation network","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.841429Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:6c4890a031dd4d4ef2195ee14775a87d052ca5f8cbe98d61f1bd37fbaee3b9fb","observation_id":"8140a34a-033f-4807-b345-621a103d42c3","resolution":{"observed_at":"2026-08-07T14:35:25.841429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.845383Z","title":"Muse-vae: Multi-scale vae for environment-aware long term trajectory prediction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.845383Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:a5fa86a3853c379821a14b67c0fb12c75ef3dda1a34a8a4ca5fa95683631c21b","observation_id":"3b58cd85-e251-4899-aa55-c1b80482459b","resolution":{"observed_at":"2026-08-07T14:35:25.845383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.849116Z","title":"Crowds by example","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.849116Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:615f03f2c9f383d16c23eaf793a81164873a80eebdd1f4fc8a7dd7600e6cac29","observation_id":"14f7ded9-0446-47e5-8237-1712548e9ace","resolution":{"observed_at":"2026-08-07T14:35:25.849116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.853137Z","title":"Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.853137Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:55a5585e5dc273fa68a1199f1b0b0b643b1fb49cd41dfc19f7aa7daed842e8de","observation_id":"1e0b6fa3-2e74-468a-987c-887017ca3bc9","resolution":{"observed_at":"2026-08-07T14:35:25.853137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.856983Z","title":"A deep spatiotemporal perspective for understanding crowd behavior","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.856983Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:d95704083b0856d8c1a146254c6361ff9d8aa25d3ace2de209c8ad221a577817","observation_id":"e4ec1ee7-f20c-4b90-9a4b-206bf3f91bb0","resolution":{"observed_at":"2026-08-07T14:35:25.856983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.860969Z","title":"Ptp-stgcn: pedestrian trajectory prediction based on a spatio-temporal graph convolutional neural network","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.860969Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:7c3d3eb35ccb595595cf46a80bd296978e3375a9a8560ae6962a74273081d508","observation_id":"3c5f6887-21bd-4d69-a1a7-08f271037bad","resolution":{"observed_at":"2026-08-07T14:35:25.860969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.864826Z","title":"Peeking into the future: Predicting future person activities and locations in videos","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.864826Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:1f88574c27d451a09273476365e6fd38440b405b8c82235619cd8648a8b01748","observation_id":"1e349869-2567-457d-a107-1cb7e3e9cab6","resolution":{"observed_at":"2026-08-07T14:35:25.864826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.868977Z","title":"Progressive pretext task learning for human trajectory prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.868977Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:04ceccf68a5980f11b996708f7ce3d881b10f1d128f9a02e6e9b9fc384f8f74e","observation_id":"c785ccf9-2fb7-40cd-a252-c7126a7817bd","resolution":{"observed_at":"2026-08-07T14:35:25.868977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.873154Z","title":"Intention-aware denoising diffusion model for trajectory prediction","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.873154Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:788126da8a80c01a6e7c6fa864db1f7cb1467742efe8e76048002c0ca4b69955","observation_id":"dde3942e-c1f1-4715-b971-c0b7c3089993","resolution":{"observed_at":"2026-08-07T14:35:25.873154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.877207Z","title":"Multimodal-semantic context-aware graph neural network for group activity recognition","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.877207Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:2fa4da8b63ade4059bb7a3dd045d7252a93e7ac63e5bf6691eef44a6d6f61785","observation_id":"c6fceed4-ad11-405a-85a2-61618bed15ba","resolution":{"observed_at":"2026-08-07T14:35:25.877207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.881276Z","title":"Visual-semantic graph neural network with pose-position attentive learning for group activity recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.881276Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:4147a7fa949742973f8f5d3d9500f89f67bb88e9a907eb3fb3dd5950b9eb4f52","observation_id":"9782ca77-a895-474c-b396-8d5d5aee94fc","resolution":{"observed_at":"2026-08-07T14:35:25.881276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.885225Z","title":"Attention-aware social graph transformer networks for stochastic trajectory prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.885225Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:c7087562a8b3aadb1d9fe7f5535940a7b557dad0c3c192b5fc87a20c4fbd98c8","observation_id":"9d327706-a7f8-481d-a3b0-0f2e8a4a7fdf","resolution":{"observed_at":"2026-08-07T14:35:25.885225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.889876Z","title":"Knowledge-aware graph transformer for pedestrian trajectory prediction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.889876Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:3eb20bd35e49f3a4e85d112e6438d665952896cb7d466321e7d1f9526c6100f5","observation_id":"c7be628e-5249-445c-801e-3ebb0d019d1c","resolution":{"observed_at":"2026-08-07T14:35:25.889876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.893828Z","title":"Video swin transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.893828Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:342324ff4785c8239517d99c8f940a29630f7791e78cc58caad6f1d169f32a3f","observation_id":"eccf7ac5-c13e-4509-978b-cccc3611b648","resolution":{"observed_at":"2026-08-07T14:35:25.893828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.897741Z","title":"Graphic-graph-based representation for analyzing people’s high-level interactions in crowds","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.897741Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:2b54c284e94c57ae78950f59fe194634fb08d09f22c0988a0ffadb6a227fce7a","observation_id":"792b02ea-bffa-467c-ac0a-ec2e61b68101","resolution":{"observed_at":"2026-08-07T14:35:25.897741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.901716Z","title":"Agent-based human behavior modeling for crowd simulation","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.901716Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:63db1c4e8e7909fbfc7d9a518e6bbf7eeeaa7f1446a7e42e2aa6820b9fadac2c","observation_id":"0aaffdeb-4398-4f84-abfc-2e1d8e71cd9a","resolution":{"observed_at":"2026-08-07T14:35:25.901716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.905985Z","title":"Introduction to gaussian processes","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.905985Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:295f81282468f98495a766dc8c3a6ac9b5bd5779c6b09862a8e0e485b0f165db","observation_id":"3aa0b813-ecc7-4339-b04d-b741f1f559ca","resolution":{"observed_at":"2026-08-07T14:35:25.905985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.910046Z","title":"Deep residual network with subclass discriminant analysis for crowd behavior recognition","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.910046Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:9130d9844fb2e32dfdbde78a5871badbafde37ec827ef15535270b4e4880a930","observation_id":"eb983051-b67b-472e-85e8-37a33eb407c0","resolution":{"observed_at":"2026-08-07T14:35:25.910046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.914378Z","title":"It is not the journey but the destination: Endpoint conditioned trajectory prediction","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.914378Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:700dfe3f7d2c1dbbdbbe32c7ed5c3ae580430158bcbff37a05c65fdff054c8a6","observation_id":"59f49e9f-8c85-4c21-ac4b-71b71649358e","resolution":{"observed_at":"2026-08-07T14:35:25.914378Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.918286Z","title":"Leapfrog diffusion model for stochastic trajectory prediction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.918286Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:e74cc2773373b719a6c9739d85a72f7cb43a74b390b88ac9a00dc37251a9cbab","observation_id":"718e054e-2fe2-4ec1-89c7-4f962e1cdb0d","resolution":{"observed_at":"2026-08-07T14:35:25.918286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.922233Z","title":"A new approach to dominant motion pattern recognition at the macroscopic crowd level","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.922233Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:2792c9764dcb951c9fe1a80ab204736f1456d87420b8c122af2cc8f1d84f221d","observation_id":"43983377-d2e3-4e91-8d9a-00cc01ca70bf","resolution":{"observed_at":"2026-08-07T14:35:25.922233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.926145Z","title":"Pi-neugode: Physics-informed graph neural ordinary differential equations for spatiotemporal trajectory prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.926145Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:3488ffcd99106d3abc275ec4179c9a3267282ad11fc62be5f6434f6ba527b90e","observation_id":"4fad6e52-5414-46de-ab74-8cc64c226c00","resolution":{"observed_at":"2026-08-07T14:35:25.926145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.930047Z","title":"Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.930047Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:9fb563db947a93811b7900b0be19e8964000dceae5a8ac5eea25bbf18a238faf","observation_id":"6bac9fbf-4bf6-47a1-87cc-9c5188119e2b","resolution":{"observed_at":"2026-08-07T14:35:25.930047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.934203Z","title":"Dag-net: Double attentive graph neural network for trajectory forecasting","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.934203Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:8174ddd577c36041cafd53784c9ce4665fad8b85bb6c668247b25aaf6067979d","observation_id":"5a05e283-4840-4dcc-8ed1-40e7b4c8efba","resolution":{"observed_at":"2026-08-07T14:35:25.934203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.938209Z","title":"The group and crowd analysis interdisciplinary challenge","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.938209Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:f7164681594fe0f2a722dc8da29135f39c8c0cd4bc87c08e9e5b4e52bbca06f8","observation_id":"41b808a6-d2d6-4cfd-a061-ba0445a802db","resolution":{"observed_at":"2026-08-07T14:35:25.938209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.942232Z","title":"Convolutional neural network for trajectory prediction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.942232Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:92a61787c7b962892224ba265425ac61cab9faadd72e023b59cd8b663688ead3","observation_id":"44ae922e-1e8e-4b83-bf30-c7e3a693da0c","resolution":{"observed_at":"2026-08-07T14:35:25.942232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.945991Z","title":"You'll never walk alone: Modeling social behavior for multi-target tracking","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.945991Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:1d4278002b662154a449502d22df820a457e3a0b8be9d836fc3b56dbc6ba1853","observation_id":"b0166f7d-bf5b-4be8-aaeb-d5c769f10b22","resolution":{"observed_at":"2026-08-07T14:35:25.945991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.949949Z","title":"G tv-l1 optical flow estimation image process","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.949949Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:546de5c714de960bcaf10a3bca9e799be605d5c5a8bb7a9eb087fd1ae9c1ba7e","observation_id":"76999736-803f-404c-974f-16cc490a6315","resolution":{"observed_at":"2026-08-07T14:35:25.949949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.953675Z","title":"Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.953675Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:9ab3cd2032e3cf6201b0a1272ea4fec40ed7da9dfb6b7aadff2617087f26f52a","observation_id":"1518078c-a046-42a9-a29d-07920a77c695","resolution":{"observed_at":"2026-08-07T14:35:25.953675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.957911Z","title":"Autonomous vehicles that interact with pedestrians: A survey of theory and practice","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.957911Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:561298189d96e7e5899c1f146f17319053ff0fb79be837dafcebb898d2d268d2","observation_id":"8c1fccfc-4d80-4f48-b168-2b24f723242d","resolution":{"observed_at":"2026-08-07T14:35:25.957911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.961858Z","title":"Real-time crowd behavior recognition in surveillance videos based on deep learning methods","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.961858Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:7df1f1efb6e0c753c10bddd8ed0076c5181efcf03c92aa657684be51efd93005","observation_id":"38d2d6b3-3bbc-4a12-9ec7-01a213e39bb1","resolution":{"observed_at":"2026-08-07T14:35:25.961858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.965906Z","title":"Scene compliant trajectory forecast with agent-centric spatio-temporal grids","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.965906Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:680ad63b907a5fb8751222cc8ca09b2a733c0df6d4ea4e32114c800d61d9fcb7","observation_id":"5636439d-25c0-4085-adf7-25fe5544a012","resolution":{"observed_at":"2026-08-07T14:35:25.965906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.969862Z","title":"Learning social etiquette: Human trajectory understanding in crowded scenes","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.969862Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:bb8c041d9a974c12a846f4c5004e751c2f214a8b01ca41d4548d2043bab4dbb1","observation_id":"52f8e985-65bf-4511-8399-1045346ba955","resolution":{"observed_at":"2026-08-07T14:35:25.969862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:35:25.973511Z","title":"Parallel distributed processing, volume 1: Explorations in the microstructure of cognition: Foundations","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:25.973511Z"},"links":{"citing_paper":"/paper/2505.18401"},"observation_digest":"sha256:17c9c518413c429f021eeed0d0454cd9ece43bc51a6907083f008e09982def7a","observation_id":"aaacc2d1-8f5c-4e64-8e9e-465f72a18dbe","resolution":{"observed_at":"2026-08-07T14:35:25.973511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.18401","last_updated":"2025-05-23T22:08:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T07:18:20.613365Z","submitted_at":"2025-05-23T22:08:35Z","title":"Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":97,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":193},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 100 of 193 outbound references and 0 inbound Pith citation observations for arXiv:2505.18401."}