{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ONDK6IHR5OXNFM65FYPGTX5KTK","short_pith_number":"pith:ONDK6IHR","schema_version":"1.0","canonical_sha256":"7346af20f1ebaed2b3dd2e1e69dfaa9a92b4b21c4ed9b45ae6695a70cb1e255c","source":{"kind":"arxiv","id":"2005.04490","version":6},"attestation_state":"computed","paper":{"title":"Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guo-jun Qi, Hongkai Xiong, Huabin Liu, Nicu Sebe, Ning Xu, Rui Qian, Shizhan Liu, Tao Wang, Weiyao Lin, Yuxi Li","submitted_at":"2020-05-09T18:24:52Z","abstract_excerpt":"Along with the development of modern smart cities, human-centric video analysis has been encountering the challenge of analyzing diverse and complex events in real scenes. A complex event relates to dense crowds, anomalous individuals, or collective behaviors. However, limited by the scale and coverage of existing video datasets, few human analysis approaches have reported their performances on such complex events. To this end, we present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve (Human-centric video analysis in complex Events), for the understand"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2005.04490","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-09T18:24:52Z","cross_cats_sorted":[],"title_canon_sha256":"f527ef77d54bda2672447b23b2e855c04fc1f62831d5396ffdde142e652da2e4","abstract_canon_sha256":"ff5be42e5f422195d54a296094a00e17388ce8bc0392d2f5e34bf7036130833f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:30:22.859238Z","signature_b64":"8HDkoHsvCGKD1VT8VjkmbFAN2MtJ6wf+cRPgwfpSW3icS4Q/8GoXwU6JjFbmpCWGmPacA2EikQ2djDooWTfEBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7346af20f1ebaed2b3dd2e1e69dfaa9a92b4b21c4ed9b45ae6695a70cb1e255c","last_reissued_at":"2026-07-05T06:30:22.858726Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:30:22.858726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guo-jun Qi, Hongkai Xiong, Huabin Liu, Nicu Sebe, Ning Xu, Rui Qian, Shizhan Liu, Tao Wang, Weiyao Lin, Yuxi Li","submitted_at":"2020-05-09T18:24:52Z","abstract_excerpt":"Along with the development of modern smart cities, human-centric video analysis has been encountering the challenge of analyzing diverse and complex events in real scenes. A complex event relates to dense crowds, anomalous individuals, or collective behaviors. However, limited by the scale and coverage of existing video datasets, few human analysis approaches have reported their performances on such complex events. To this end, we present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve (Human-centric video analysis in complex Events), for the understand"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.04490","kind":"arxiv","version":6},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.04490/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2005.04490","created_at":"2026-07-05T06:30:22.858792+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.04490v6","created_at":"2026-07-05T06:30:22.858792+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.04490","created_at":"2026-07-05T06:30:22.858792+00:00"},{"alias_kind":"pith_short_12","alias_value":"ONDK6IHR5OXN","created_at":"2026-07-05T06:30:22.858792+00:00"},{"alias_kind":"pith_short_16","alias_value":"ONDK6IHR5OXNFM65","created_at":"2026-07-05T06:30:22.858792+00:00"},{"alias_kind":"pith_short_8","alias_value":"ONDK6IHR","created_at":"2026-07-05T06:30:22.858792+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.08227","citing_title":"New VVC profiles targeting Feature Coding for Machines","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK","json":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK.json","graph_json":"https://pith.science/api/pith-number/ONDK6IHR5OXNFM65FYPGTX5KTK/graph.json","events_json":"https://pith.science/api/pith-number/ONDK6IHR5OXNFM65FYPGTX5KTK/events.json","paper":"https://pith.science/paper/ONDK6IHR"},"agent_actions":{"view_html":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK","download_json":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK.json","view_paper":"https://pith.science/paper/ONDK6IHR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.04490&json=true","fetch_graph":"https://pith.science/api/pith-number/ONDK6IHR5OXNFM65FYPGTX5KTK/graph.json","fetch_events":"https://pith.science/api/pith-number/ONDK6IHR5OXNFM65FYPGTX5KTK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK/action/storage_attestation","attest_author":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK/action/author_attestation","sign_citation":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK/action/citation_signature","submit_replication":"https://pith.science/pith/ONDK6IHR5OXNFM65FYPGTX5KTK/action/replication_record"}},"created_at":"2026-07-05T06:30:22.858792+00:00","updated_at":"2026-07-05T06:30:22.858792+00:00"}