{"as_of":"2026-08-11T20:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4411a486cd6ff2eb4062f03e94fda9aafa334df98a003d09b5e7ef7b0026e7a1","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T22:21:36.395963Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2502.04566/citation-record","integrity":"/paper/2502.04566/integrity","json":"/paper/2502.04566/citation-record.json","paper":"/paper/2502.04566"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:37.067667Z","title":"Data- driven intelligent transportation systems: A survey,","venue":null,"work_id":"8b531783-1007-455a-9a20-2c7b9d582597","year":2011},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.270085Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:04394511f8f60ba528c1a43d9cc60188c91acfc08e3c498b27d930e1fc2ca9e6","observation_id":"ba9d7acc-338e-42dc-b6a7-b646b0a34680","resolution":{"observed_at":"2026-08-08T22:21:37.071543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:37.057065Z","title":"Traffic accident prediction using 3-d model-based vehicle tracking,","venue":null,"work_id":"cdead42f-b0ca-4a7a-ba82-2fd8de390d4e","year":2004},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.273803Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:d80e49852f9832aba1c65ee3de2e5b6c8ff0bbdd8567c473302f3a0089adbbb4","observation_id":"cd157e8c-9a45-491a-9e87-5f94153c6edd","resolution":{"observed_at":"2026-08-08T22:21:37.060224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:37.046424Z","title":"Vision-based active safety system for automatic stopping,","venue":null,"work_id":"3922303b-1aef-4eb6-89dd-2929c895120f","year":2012},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.276832Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:a2b73e12d07acdd8d044fcc0907e15481787da5cfcd3e2161226cbe6b9c0ca35","observation_id":"b2c107cc-6376-427d-9b49-fb94bed1b239","resolution":{"observed_at":"2026-08-08T22:21:37.049580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:37.035936Z","title":"Vehicle ego-motion estimation and moving object detection using a monocular camera,","venue":null,"work_id":"adb33084-bba4-4135-b7fb-186f6bbd7ffc","year":2006},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.279867Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:10e2f599420ebd6edc28dc75c29b95cdccc44f88fc7e811c374b0b715a55c905","observation_id":"de4817e2-20fb-480a-bf10-68b2c563e373","resolution":{"observed_at":"2026-08-08T22:21:37.039159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:37.026696Z","title":"Vehicle detection and tracking techniques: A concise review","venue":null,"work_id":"c8dd9a51-c833-40ec-9205-044c8febe0f2","year":null},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.283087Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:ec9e6a54a45f72638ab9837ca8ff522d3ff284e8a1e070a2bc6e89ed4916c99c","observation_id":"a2f450f3-20ff-4374-bafb-480d457228cb","resolution":{"observed_at":"2026-08-08T22:21:37.029835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:37.015801Z","title":"A cnn vehicle recognition algo- rithm based on reinforcement learning error and error-prone samples,","venue":null,"work_id":"1ecebc03-4877-4d4d-8f7b-3ad353e6293e","year":2018},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.286038Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:dfefd8a5ba7578ecae73e48eff02c09daeec9f79a239d025f9236aa62426310e","observation_id":"739a7ee6-8d57-4bba-87a1-d7fc5f2cdc85","resolution":{"observed_at":"2026-08-08T22:21:37.020396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:37.006217Z","title":"Using reinforcement learning with partial vehicle detection for intelligent traffic signal control,","venue":null,"work_id":"9191eb15-36a7-4b1b-a066-85e01aad5ed1","year":2020},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.288973Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:916f604b7ec36a2d14e9a7624caee1c1f8b4e3aa297704424c899ad7150ad394","observation_id":"26a66a25-f91a-48f5-a30f-1a1e23218734","resolution":{"observed_at":"2026-08-08T22:21:37.009480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-08T22:21:36.292329Z","title":"Yolov4: Optimal speed and accuracy of object detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.292329Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:09704fe58359cba624c6d9053e78cf8e068862b87b090d9139ce0c2c8ec2ec10","observation_id":"3d094e8d-77d8-4e09-bdbf-3ca1fa8a42df","resolution":{"observed_at":"2026-08-08T22:21:36.292329Z","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":"2026-08-08T22:21:36.295227Z","title":"Rich feature hierarchies for accurate object detection and semantic segmentation,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.295227Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:7f854cd65248895ccb8cf333576784da7ebf85fe14dfc394493d8099843b8ce9","observation_id":"51e3e9bf-2deb-43c2-bc26-13c33392536a","resolution":{"observed_at":"2026-08-08T22:21:36.295227Z","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":"2026-08-08T22:21:36.297955Z","title":"Fast r-cnn,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.297955Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:d9f3f7813531a33c0eedfb3c53545b747e9fe19f89da97e7a2c134b7db8cdbd9","observation_id":"6dca5b7d-f209-4468-8d59-ca9f7557be1b","resolution":{"observed_at":"2026-08-08T22:21:36.297955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.01497","last_updated":"2016-01-06T06:30:17Z","snapshot_observed_at":"2026-07-06T04:19:53.343717Z","submitted_at":"2015-06-04T07:58:34Z","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.01497","snapshot_observed_at":"2026-08-08T22:21:36.300762Z","title":"Faster r-cnn: Towards real- time object detection with region proposal networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.300762Z"},"links":{"cited_paper":"/paper/1506.01497","citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:450eb9e48593aa40bca3d5d4df30122d315a7ce9df761fc06784810ad2d019ec","observation_id":"407b23e5-0172-444a-9734-7910b245fe92","resolution":{"observed_at":"2026-08-08T22:21:36.300762Z","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":"2026-08-08T22:21:36.304005Z","title":"Ssd: Single shot multibox detector,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.304005Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:b4cc735c45bc57f7ff62f50bf1f7efc73ece91e8dd5cf28a467946e7e376beee","observation_id":"769e6721-cdea-4ade-a8b6-a87f71d9313c","resolution":{"observed_at":"2026-08-08T22:21:36.304005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06659","last_updated":"2017-01-23T22:33:35Z","snapshot_observed_at":"2026-08-10T07:36:16.889189Z","submitted_at":"2017-01-23T22:33:35Z","title":"DSSD : Deconvolutional Single Shot Detector","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.06659","snapshot_observed_at":"2026-08-08T22:21:36.307124Z","title":"Dssd: Deconvolutional single shot detector,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.307124Z"},"links":{"cited_paper":"/paper/1701.06659","citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:f2c09f00e870c3baa1527f802eb8866e56bf164044b95876491536396be0e619","observation_id":"9be9ec78-6874-4a44-a194-f31e82668b62","resolution":{"observed_at":"2026-08-08T22:21:36.307124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1605.06409","last_updated":"2023-12-11T13:28:51Z","snapshot_observed_at":"2026-08-11T08:27:18.228519Z","submitted_at":"2016-05-20T15:50:11Z","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.06409","snapshot_observed_at":"2026-08-08T22:21:36.310475Z","title":"R-fcn: Object detection via region- based fully convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.310475Z"},"links":{"cited_paper":"/paper/1605.06409","citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:8f52e14d00dd0ff76c5e6664f8b08fc2d0c388defe434dfab6564312ba87baf6","observation_id":"26bdf4cf-55d7-4124-b08b-581d8339d7b5","resolution":{"observed_at":"2026-08-08T22:21:36.310475Z","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":"2026-08-08T22:21:36.313596Z","title":"Feature pyramid networks for object detection,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.313596Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:ed1c0346fb5b5ed93e761fad37b920c3b3c7515636fc6bd9d70f52d0cd1c2f1b","observation_id":"25adcca0-2a8f-422d-bb8c-169c45f328ca","resolution":{"observed_at":"2026-08-08T22:21:36.313596Z","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":"2026-08-08T22:21:36.316425Z","title":"Focal loss for dense object detection,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.316425Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:70493b4d361aaad6d70baaafaa74a52d207c47ad004ed4a16d6de1e4c62445ea","observation_id":"ffb3defb-fa36-40dc-b702-4b7fa0de4e0e","resolution":{"observed_at":"2026-08-08T22:21:36.316425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.02767","last_updated":"2018-04-08T22:27:57Z","snapshot_observed_at":"2026-08-06T11:09:16.409556Z","submitted_at":"2018-04-08T22:27:57Z","title":"YOLOv3: An Incremental Improvement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.02767","snapshot_observed_at":"2026-08-08T22:21:36.319037Z","title":"Yolov3: An incremental improvement,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.319037Z"},"links":{"cited_paper":"/paper/1804.02767","citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:9db17d57238588a98dfedbbad373ef5474477051fea2799fb0379318ed5741ea","observation_id":"78a35fe0-382a-4276-ad90-ee418a77cd63","resolution":{"observed_at":"2026-08-08T22:21:36.319037Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.953934Z","title":"Efficientdet: Scalable and efficient Page 15 of 16 object detection,","venue":null,"work_id":"72ea62af-08a6-413f-b2a3-28ea44736e74","year":2020},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.322388Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:c2e014cf088df90ff7c2ae4cd646805aeda34ed88bf9fef4677034bc8446d818","observation_id":"81f13c6d-55a8-4bbe-b8c8-f347a76e8d5c","resolution":{"observed_at":"2026-08-08T22:21:36.957062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.944518Z","title":"Vehicle detection in urban traffic surveillance images based on convolutional neural networks with feature concatenation,","venue":null,"work_id":"35e25cae-b5ad-4065-9105-5c807cec4197","year":2019},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.325552Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:722e8ee8d349513647ebe1ddb11d56daf230bc603c72138a5694e8ee39fd3fc0","observation_id":"72678b1f-66de-40d9-93fe-ea84cf1819c3","resolution":{"observed_at":"2026-08-08T22:21:36.947786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.934985Z","title":"Yolov5 base repository,","venue":null,"work_id":"85344258-2658-4c9e-b344-63f387739296","year":2020},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.328335Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:4154c3f3dad5ca46e0bf041cbe7a970939fd9dcd338869695d2690a48fac14af","observation_id":"53b5ca51-5c2c-4be2-8edb-f5b7fc36c067","resolution":{"observed_at":"2026-08-08T22:21:36.938262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.924493Z","title":"Spherical formulation of moving object geometric constraints for monocular fisheye cameras,","venue":null,"work_id":"01b0822d-917e-44e5-8b3a-41a4a1e3f465","year":2019},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.331479Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:c222814368c6ce60bc85bb31cfca6b701319c590c8ddad2947842a81e78ab298","observation_id":"5ffc24e4-a88c-450b-bdef-7f69b45fd352","resolution":{"observed_at":"2026-08-08T22:21:36.928588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.914199Z","title":"Intelligent traffic monitoring and surveillance with multiple cameras,","venue":null,"work_id":"2ab3f622-1d06-4704-b692-a087833735a3","year":2008},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.334369Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:5a816382916ca18160be80169775370fa9089ae92478927264556a85a8f2c476","observation_id":"6d96a068-f469-46e2-aae9-88ba9fc5ffa1","resolution":{"observed_at":"2026-08-08T22:21:36.917690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.904030Z","title":"Fisheye lens camera based surveillance system for wide field of view monitoring,","venue":null,"work_id":"9107db6a-864d-4d94-9fc2-94a02853d1b2","year":2016},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.337193Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:28b33d62c378675ab65a1d8f379cfd872a472a205e90e4d7b4d929f9728e9990","observation_id":"72eaf086-cba1-4cf9-a708-2361ce0e1fd0","resolution":{"observed_at":"2026-08-08T22:21:36.907418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.892698Z","title":"Real time multi-vehicle tracking and counting at intersections from a fisheye camera,","venue":null,"work_id":"64699e60-9cdc-4227-8650-25be7d9221d8","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.339981Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:6f2cd84093ed5561c6988e821de5aed520ee979b43c63f63d708b6a7b5b2c61a","observation_id":"31f8f1a8-97cc-4fd3-87b6-c3bbea62686b","resolution":{"observed_at":"2026-08-08T22:21:36.896499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.881269Z","title":"360 detection and tracking algorithm of both pedestrian and vehicle using fisheye images,","venue":null,"work_id":"7d83c5f4-71d1-4d5a-9dad-9da61de31256","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.343186Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:c232079f68c2724fb4e4480d156ebd927560c4133675de84a06ad3b951934668","observation_id":"5e21e038-2fdf-4ca5-9158-c21192043176","resolution":{"observed_at":"2026-08-08T22:21:36.884330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.872260Z","title":"Vehicle detection in close-up range with a fisheye camera,","venue":null,"work_id":"9d81cc7d-7622-4684-8d31-893e3acd8004","year":2011},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.346280Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:dbd96f73a77003155a768315ec7e63536ca0ddd7cf5adc25bccdb6ff4931e88e","observation_id":"a115cb65-8a2a-4044-873f-99a2dc230c1f","resolution":{"observed_at":"2026-08-08T22:21:36.875273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.863254Z","title":"A blind-zone detection method using a rear-mounted fisheye camera with combination of vehicle detection methods,","venue":null,"work_id":"8be3818d-c888-4d55-8667-4481fc780481","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.349337Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:d1885660f331c604061dc18d01bacf54820bc83c89444d394fc9669cef799107","observation_id":"b1dec921-2387-452b-ba2d-12c50732e986","resolution":{"observed_at":"2026-08-08T22:21:36.866263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.854495Z","title":"Optimization of a cnn-based object detector for fisheye cameras,","venue":null,"work_id":"e7069a6a-eb9f-44a3-8521-7c419c919f08","year":2019},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.352353Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:a8a164780977ef078c958609fb68411cbe2b175e4e67abf3e9e9e1ce7099c243","observation_id":"f5035266-82b2-407c-aa5b-be4ebb2cc301","resolution":{"observed_at":"2026-08-08T22:21:36.857494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.843306Z","title":"Cspnet: A new backbone that can enhance learning capability of cnn,","venue":null,"work_id":"22beda3e-961c-488b-b62e-8f1132fc0aba","year":2019},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.355484Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:87d93f1dfbc18d89490ad18ed513fe647ecbe1798e616a39f1fbfc249511e670","observation_id":"24afef7b-6452-41a5-b0e2-ff4dca040bd4","resolution":{"observed_at":"2026-08-08T22:21:36.846946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-08T22:21:36.358325Z","title":"Spatial pyramid pooling in deep convolutional networks for visual recognition,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.358325Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:c9b18a8ab164e0b65940d65f5b4b6ba0799e28e70073b935fd9dc1c2d729c7c1","observation_id":"ae5c2416-f0b9-44cf-bd89-53fbda9cbf77","resolution":{"observed_at":"2026-08-08T22:21:36.358325Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.684215Z","title":"Path aggregation network for instance segmentation,","venue":null,"work_id":"8459fc02-fbcd-4fb8-8509-38d00554db85","year":2018},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.361154Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:83377283443cfcb559cb5778875f86fd72cbf265fa488ac574689febbe0ba29b","observation_id":"4f857502-05a0-4d0b-b2aa-2907ccd19812","resolution":{"observed_at":"2026-08-08T22:21:36.690352Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-08T22:21:36.363932Z","title":"Microsoft coco: Common objects in context,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.363932Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:89eb32e634fedd8232c235206bb1a5470c54e2ea8322b62ad3ecc3183291ae8f","observation_id":"d3590877-d583-4197-8d4d-2b02d1cf6392","resolution":{"observed_at":"2026-08-08T22:21:36.363932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.04687","last_updated":"2020-04-08T09:25:06Z","snapshot_observed_at":"2026-08-08T14:55:35.331716Z","submitted_at":"2018-05-12T09:24:21Z","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.04687","snapshot_observed_at":"2026-08-08T22:21:36.367101Z","title":"Bdd100k: A diverse driving video database with scalable annotation tooling,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.367101Z"},"links":{"cited_paper":"/paper/1805.04687","citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:20f1e6db7fd07ae9f19e7b6055d923acd1f92f715b0c01b6f692993b51cb0b61","observation_id":"c152299d-08cc-45c4-adf3-a984aa36d8aa","resolution":{"observed_at":"2026-08-08T22:21:36.367101Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.823948Z","title":"9mp 360-degree camera, fisheye : Interchange in thailand,","venue":null,"work_id":"33ed8869-fa11-4366-acf3-5849a0271ccf","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.370597Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:b2e0c256bdaefee2d3eb37dcbf133f601e890d20cdf0ebc5ac62e916bcd79ba0","observation_id":"3afb3421-ac4b-4fb6-8bd8-07e4dad6e412","resolution":{"observed_at":"2026-08-08T22:21:36.827917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.812194Z","title":"9mp 360-degree camera, fisyeye : Intersection in thailand no.2,","venue":null,"work_id":"5f505942-a214-405c-88aa-ec64034b501a","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.373397Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:a1d260a27f945ef8c268e465e2f1afcd18bb4d79e79a262b259ea8b5a538748c","observation_id":"76236282-fb66-46b0-a5b5-48b751f2278f","resolution":{"observed_at":"2026-08-08T22:21:36.815744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.802368Z","title":"Cropping example of 9mp 360-degree camera (road),","venue":null,"work_id":"0452b064-4ea6-472d-a65f-51f20232c728","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.376321Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:fd4d5dd74d0b9f43d39c907ba25e8fda6f62f112fc922aeed2ded7a318f37b2e","observation_id":"5876cd4f-6df4-4e4a-902e-f331bb260f4a","resolution":{"observed_at":"2026-08-08T22:21:36.805527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.792652Z","title":"9mp 360-degree camera, fisheye : Road in thailand,","venue":null,"work_id":"75c0f3f3-a780-4a09-99db-cc943b6fb97d","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.379964Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:66d0a8d6b969a6d95f8f99aa2590ef7f06fab19512b06801eb0e9b33ebd35aa9","observation_id":"9c2334fb-2a47-4657-a5eb-1994cf0a16ec","resolution":{"observed_at":"2026-08-08T22:21:36.796027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.782939Z","title":"6.6 9mp 360 degree camera, fisyeye intersection in thailand,","venue":null,"work_id":"5afad68c-0ccd-4178-ac12-4c2f8a608f24","year":2015},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.383252Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:22bcfa8d35956eb7224ab8a7f2e6d973fbfbb1d3dc5239b4dc08eabee1a7fe5a","observation_id":"3a0c2958-1063-4dc2-b0b1-eb4b1d41f5e4","resolution":{"observed_at":"2026-08-08T22:21:36.786284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.773182Z","title":"Self-training with noisy student improves imagenet classification,","venue":null,"work_id":"e99bf60e-e293-4e12-8654-ae701df24d2c","year":2020},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.386737Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:26edc6da29851bd3f8657f1b9a8db6ddcede7afeb8921602de6731b2b1ea4f56","observation_id":"142d4eca-176d-4352-83ad-a7411af14a06","resolution":{"observed_at":"2026-08-08T22:21:36.776546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-08T22:21:36.390054Z","title":"The pascal visual object classes (voc) challenge,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.390054Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:ebd3f20e67e208454d09b8da0e5891a0eb28639f0865efda25567f0de3c1e9a4","observation_id":"5b12999d-9222-41dd-b81a-7beadd4cd32a","resolution":{"observed_at":"2026-08-08T22:21:36.390054Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.755056Z","title":"Generalized intersection over union: A metric and a loss for bound- ing box regression,","venue":null,"work_id":"25882a48-7dc1-4943-81cd-61a4c242b871","year":2019},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.393131Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:b752864184ce57d3518817e5af177a8ad41c6e514c93149d6918b2bda1fcebd8","observation_id":"7d12e7fd-d199-4561-99b9-43d58cf35c32","resolution":{"observed_at":"2026-08-08T22:21:36.758484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:36.744443Z","title":"Distance-iou loss: Faster and better learning for bounding box regression,","venue":null,"work_id":"0ecca118-d0d6-4031-97d7-b48f0c7e64de","year":2020},"citing_paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:36.395963Z"},"links":{"citing_paper":"/paper/2502.04566"},"observation_digest":"sha256:be5949cd20a24d4cb0b6016374fb99df9e6f0b1aa962f03549646dcc8ecccac2","observation_id":"1ad040ef-5c35-4b22-a64d-d3ad7b2796a3","resolution":{"observed_at":"2026-08-08T22:21:36.747810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.04566","last_updated":"2025-02-06T23:42:05Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T15:05:07.804396Z","submitted_at":"2025-02-06T23:42:05Z","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":27},"total_outbound_references":42},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2502.04566."}