{"as_of":"2026-08-14T18:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:02da194ca8f1d6a8a21d0a36a251efe56d0f161f597b1d8e126c53a2e7d2baf0","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:43:54.194697Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"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/2501.00843/citation-record","integrity":"/paper/2501.00843/integrity","json":"/paper/2501.00843/citation-record.json","paper":"/paper/2501.00843"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:53.876543Z","title":"Simple online and realtime tracking,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:53.876543Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:083de094fbc8705203005fb2f3477176ceef35f6f2c7e62ae645ce6bf7c5ffee","observation_id":"c6f41dd6-eea1-40fd-857d-825ba6121553","resolution":{"observed_at":"2026-08-10T22:43:53.876543Z","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":"2026-08-10T22:43:54.844857Z","title":"Simple online and rea ltime tracking with a deep association metric,","venue":null,"work_id":"6469dd78-7af2-41e9-9609-32e7fdf38a1f","year":2017},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:53.881113Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:33fdb3a43e6bec9b47f1f6aed69bd132df8ca6e8408cdf73410fbd4cc1c8cf02","observation_id":"7d898a5c-c29f-42c5-9548-5c6751c88580","resolution":{"observed_at":"2026-08-10T22:43:54.852117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-10T22:43:53.885652Z","title":"Strong- sort: Make deepsort great again,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:53.885652Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:ac89312e5dbb46c5708d87c4ac2ff50d9611bd30f8f27b347b2e28a113d52b2d","observation_id":"e2bf590e-8fca-48bd-b582-350ffc5810e3","resolution":{"observed_at":"2026-08-10T22:43:53.885652Z","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":"2026-08-10T22:43:54.815799Z","title":"ByteTrack: Multi-object tracking by associat ing every detection box,","venue":null,"work_id":"743bc293-47de-40d8-a883-49388875fc59","year":2022},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:53.890833Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:a87a52f3b2ce3a2210e882fa17625a2dc89ae8ccac3ea2fd36e265958d739874","observation_id":"17b16855-7ada-4f59-bce8-da70bac24deb","resolution":{"observed_at":"2026-08-10T22:43:54.820065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2206.14651","last_updated":"2022-07-07T15:36:49Z","snapshot_observed_at":"2026-08-13T15:14:12.197118Z","submitted_at":"2022-06-29T13:45:03Z","title":"BoT-SORT: Robust Associations Multi-Pedestrian Tracking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.14651","snapshot_observed_at":"2026-08-10T22:43:54.030577Z","title":"BoT-SORT: Ro bust associations multi-pedestrian tracking,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.030577Z"},"links":{"cited_paper":"/paper/2206.14651","citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:8c8ad81b6f01162eccde985e2c96eeddd43f29d4fbeaf4a70c60e255e2a8425f","observation_id":"fc3aa5ac-b837-4381-84cd-4e35ba0d7a34","resolution":{"observed_at":"2026-08-10T22:43:54.030577Z","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":"2026-08-10T22:43:54.795528Z","title":"Simple cues lead to a strong multi-object tracker,","venue":null,"work_id":"cb71ad76-2d72-4f9d-842c-c1d9ad469d71","year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.036071Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:7db954941ad0d9d9515dcd1490ab17a8238cba1046ab504b88cd7d8b0a2d2ca6","observation_id":"f826915b-3640-4797-8f96-1a9e059e8a92","resolution":{"observed_at":"2026-08-10T22:43:54.799227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.782522Z","title":"Occlusion-robust online multi-object vis ual tracking using a GM-PHD ﬁlter with CNN-based re- identiﬁcation,","venue":null,"work_id":"31b5697b-4eb0-423f-9f7b-88f4da133a9b","year":2021},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.043289Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:7ff101ce41ea778d2406c23de412377b135f3e4fd864b5f914f9f2c965173f11","observation_id":"3ba3a5f4-edb6-413c-a6c1-ec4d800c93d1","resolution":{"observed_at":"2026-08-10T22:43:54.786843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.770241Z","title":"An introduction to the kalman ﬁlt er,","venue":null,"work_id":"5aa68e52-f893-4bee-bd7b-58ed170844ca","year":2006},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.048374Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:c5d40626ea1cb173b24204d6d07ad5009ebc0a8d3b86a0d000d47c6289fb1bdb","observation_id":"e313d042-a8ba-403b-9b99-cebdd1e56c7a","resolution":{"observed_at":"2026-08-10T22:43:54.774536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.754637Z","title":"A detailed study of the association task in t racking-by- detection-based multi-person tracking,","venue":null,"work_id":"57b4850b-5f9a-478e-8ec7-865d9cb7f637","year":2022},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.053157Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:756bc8fdbe4a5afa6fa96053e4878fda54456e3922a794b85e9679892d00f59f","observation_id":"8b77b4aa-45da-4aa1-ac3d-24b55b206dcb","resolution":{"observed_at":"2026-08-10T22:43:54.759070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.740630Z","title":"T he mahalanobis distance,","venue":null,"work_id":"da35c076-9566-4efb-9dcb-7d292967163b","year":2000},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.057579Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:7ff1058f44da41dee6c0e0d75ccd5b92ae23517b834dbf7d9c2d2324a4b805d8","observation_id":"9ce54b8d-d38e-4f4b-b2a2-d93686bca2d7","resolution":{"observed_at":"2026-08-10T22:43:54.745335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1783.36134","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.453934Z","title":"FastReID: A pytorch toolbox for general instance re-ident iﬁcation,","venue":null,"work_id":"e90a9216-2504-40c1-a8bf-ced066071fae","year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.063122Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:efa942ac9fe630baeac45d9864ce25626df7e4993073f6ad02ab62536b9f31b8","observation_id":"949a9e57-4e51-4c3f-ac59-036b8b02f807","resolution":{"observed_at":"2026-08-10T22:43:54.461023Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.726550Z","title":"Local-aware global attention network for person re-identiﬁcation based on body and hand images,","venue":null,"work_id":"44d92619-4650-4d34-b901-010332d6bbec","year":2024},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.069766Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:22aee2af9c08d38d3632d67cee4d899860c512c009627e52c4802090c8b6d45d","observation_id":"de1d5767-8b11-47c9-91d2-d64736058111","resolution":{"observed_at":"2026-08-10T22:43:54.731149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.715768Z","title":"Ob servation- centric sort: Rethinking sort for robust multi-object trac king,","venue":null,"work_id":"ab9c284c-96e9-4910-9a4f-312118131fc0","year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.073978Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:b93bbb5449f2d76f1a178457ddec7b6644c5dde2ddee2c075b1a1a788bc4613b","observation_id":"bad5277f-b288-4ae6-b484-bf1437bfc2c4","resolution":{"observed_at":"2026-08-10T22:43:54.719250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2302.11813","last_updated":"2023-02-23T06:51:07Z","snapshot_observed_at":"2026-08-14T10:48:03.026508Z","submitted_at":"2023-02-23T06:51:07Z","title":"Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.11813","snapshot_observed_at":"2026-08-10T22:43:54.078568Z","title":"Deep-OC -SORT: Multi-pedestrian tracking by adaptive re-identiﬁcation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.078568Z"},"links":{"cited_paper":"/paper/2302.11813","citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:eb9ec68cdde53748994210e5058afcc9f04a075e209b5deab8c3072887ed5e28","observation_id":"51585f97-ebc4-48d8-b60f-1667e0f5505b","resolution":{"observed_at":"2026-08-10T22:43:54.078568Z","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":"2026-08-10T22:43:54.701628Z","title":"Towards rea l-time multi-object tracking,","venue":null,"work_id":"75ec0862-eab6-49ea-ab78-3d813cba8f54","year":2020},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.083236Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:dc0a7dc5a79f798c5ea5ca88f34673c80b1ed283d43afa0473edaf79a2889437","observation_id":"eb23e4d2-d666-4863-8c59-70c9451e3292","resolution":{"observed_at":"2026-08-10T22:43:54.705441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"10.1007/s11263-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.248292Z","title":"FairMOT: On the fairness of detection and re-identiﬁcation in multiple obj ect tracking,","venue":null,"work_id":"469cf07c-e6c6-4522-bfc1-81b86d1cdc2a","year":2021},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.087902Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:3035b6833135430788742fc3e5dad96a6f5c7e9df51ad0a18676baf2321cd743","observation_id":"aa06188d-98cf-4d2a-afe8-eba419015ebe","resolution":{"observed_at":"2026-08-10T22:43:54.254945Z","resolver_source":"doi","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.688366Z","title":"An improved association pip eline for multi- person tracking,","venue":null,"work_id":"629b0b23-1ddb-49c2-b92f-11ed629ba65f","year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.092429Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:4be01f0a3198c6e8474954a5a9ad44761f4d078c39ef8fb699ed4a28bc75cfc4","observation_id":"30c70e65-40ca-4f8b-acd2-3cbe80b60eed","resolution":{"observed_at":"2026-08-10T22:43:54.693005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.676024Z","title":"Hybrid-SORT: Weak cues matter for online multi-object tra cking,","venue":null,"work_id":"3e2b8b69-d9f9-46eb-bbb2-c2d95f72c208","year":2024},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.097023Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:73263da250a4ef8d4029e66fcaeb3ef5a00a23fc345bd373e8ae8de4118572f2","observation_id":"83711008-6b3f-445a-9fdc-722ea1bd58e2","resolution":{"observed_at":"2026-08-10T22:43:54.680331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.663846Z","title":"POI: Multip le object tracking with high performance detection and appearance fe ature,","venue":null,"work_id":"969d5044-71f7-49c7-9530-663249d70683","year":2016},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.101810Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:b6db1f821230243b77a477548169338de49e41c3a4d14a3600b686ec9719f4d9","observation_id":"3938c0bf-47c3-45cd-81a7-86fcade032d0","resolution":{"observed_at":"2026-08-10T22:43:54.668656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.653140Z","title":"Real-time m ultiple people tracking with deeply learned candidate selection an d person re- identiﬁcation,","venue":null,"work_id":"1fefa22e-33e9-4121-9be5-f67ed093df1b","year":2018},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.106791Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:fd583fdc0290f5c9e3a88311edd0e7ef21de69e7caadd1c7d6cb17783254514a","observation_id":"4349c905-b8dd-4bc1-ae73-69abd5edb1c7","resolution":{"observed_at":"2026-08-10T22:43:54.656645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.642501Z","title":"Online multi-object visual tracking usin g a GM-PHD ﬁlter with deep appearance learning,","venue":null,"work_id":"d7cea177-9032-4c58-b933-7a1beefcac89","year":2019},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.111359Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:cf649a20d9be120c7dabb6bdacb8379ce96604da2c778c0f2c4314c5088fff5f","observation_id":"46df4343-19cd-46df-9cbb-aa114b136856","resolution":{"observed_at":"2026-08-10T22:43:54.646072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.630450Z","title":"GM-P HD ﬁlter based online multiple human tracking using deep discrimina tive corre- lation matching,","venue":null,"work_id":"d91eaae1-fdab-4e0e-bd54-6a6f306b8cd9","year":2018},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.115577Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:cb8deee7425604fbb28c5c870cf81c889887371de3575bf3621e61a9666da4aa","observation_id":"0b602321-651b-4976-ac0a-b46ca4e0f545","resolution":{"observed_at":"2026-08-10T22:43:54.635038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.616977Z","title":"Development of a N- type GM-PHD ﬁlter for multiple target, multiple type visual tracking,","venue":null,"work_id":"69360121-452f-4c3c-98e3-7dd39310f12f","year":2019},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.120184Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:ea9eaaf0e6ed87bf3d3201e3a8a5a497de4a1b0b72ebbf5a3ebf4b6019c23cb0","observation_id":"9142627a-eda4-4f16-a157-ab1a4e1a2a40","resolution":{"observed_at":"2026-08-10T22:43:54.621870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.603800Z","title":"Fo cus on details: Online multi-object tracking with diverse ﬁne-gr ained represen- tation,","venue":null,"work_id":"6f8df591-1c3c-4292-adf4-98d298bcbfe5","year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.125928Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:0ff6510987021c65e97bbf366c5411e2e966e654d86b799b0ce79acc2ec15e95","observation_id":"3a82a8ff-1690-460c-bd43-fcc1a43aecf7","resolution":{"observed_at":"2026-08-10T22:43:54.608095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.590298Z","title":"UTM: A uniﬁed multip le object tracking model with identity-aware feature enhance ment,","venue":null,"work_id":"105fb68c-6f6e-4bbc-b63d-1b68c7f68b29","year":2023},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.129799Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:d97517c7f33ab678c2aa0bedb1657b53337cd050d74effdfb0e85249cab32747","observation_id":"a87d65cc-0746-465b-92be-50c2d85e9f85","resolution":{"observed_at":"2026-08-10T22:43:54.594664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-10T22:43:54.136124Z","title":"FeatureSOR T: Essential features for effective tracking,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.136124Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:b23df737e1d62f0408b4af0f2e93db85bdf3372168837187317c7096ac7b6ecb","observation_id":"bf5545ec-2743-4fa0-8236-81dbc1b3b2ae","resolution":{"observed_at":"2026-08-10T22:43:54.136124Z","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":"2026-08-10T22:43:54.140415Z","title":"The hungarian method for the as- signment problem,","venue":null,"work_id":null,"year":1955},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.140415Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:d48e72aa893840f3c6bcbd6f58b3b8fbf697c6d1124f40d51deb98e51761ac1a","observation_id":"20a41cdd-c176-4dd9-b50c-c2b1c708ce71","resolution":{"observed_at":"2026-08-10T22:43:54.140415Z","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":"2026-08-10T22:43:54.576882Z","title":"Tracki ng without bells and whistles,","venue":null,"work_id":"b93da934-1ccf-4075-8fe9-764b7b1f1ed2","year":2019},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.144354Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:c62706e5e2a44466de4d70d7d8b2ab365dbc37608fe9ab0f979d1fb029c7e466","observation_id":"430b3735-87ed-4502-b85f-1be163d5b969","resolution":{"observed_at":"2026-08-10T22:43:54.581459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2005.00844","last_updated":"2020-10-20T22:05:50Z","snapshot_observed_at":"2026-08-07T17:12:56.935382Z","submitted_at":"2020-05-02T14:40:18Z","title":"Derivation of a Constant Velocity Motion Model for Visual Tracking","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00844","snapshot_observed_at":"2026-08-10T22:43:54.149969Z","title":"Derivation of a constant velocity motion m odel for visual tracking,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.149969Z"},"links":{"cited_paper":"/paper/2005.00844","citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:379d24750b2ca7994178120217efd37eaedf1b9d2cd1146a010a8cd3dc851b7e","observation_id":"24fab2c8-1f72-4dfa-85f6-6d8ade86c7ef","resolution":{"observed_at":"2026-08-10T22:43:54.149969Z","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":"2026-08-10T22:43:54.562683Z","title":"Bag of tricks and a strong baseline for deep person re-identiﬁcation,","venue":null,"work_id":"0eba9bdb-829a-4678-a957-cd15798e39a6","year":2019},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.154128Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:a2775d28ce8d7218cdd78ed9d19300ede4176275e92047b066b50bef4c4e6a36","observation_id":"d72fe862-2c89-42ab-bca5-1ffc1688a486","resolution":{"observed_at":"2026-08-10T22:43:54.567721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.549135Z","title":"ResNeSt: Split -attention networks,","venue":null,"work_id":"e58f5d42-2aba-4617-ab8f-a9fbe5fd396f","year":2022},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.158094Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:12712b9295d2e7a121937257ee5f1e0224c4bc27da472eeec7b8e5096d13671c","observation_id":"182c2b5d-fbe1-498c-83a3-884d0a77d0f8","resolution":{"observed_at":"2026-08-10T22:43:54.553221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1603.00831","last_updated":"2016-05-03T23:55:38Z","snapshot_observed_at":"2026-08-03T05:59:06.882015Z","submitted_at":"2016-03-02T19:07:56Z","title":"MOT16: A Benchmark for Multi-Object Tracking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.00831","snapshot_observed_at":"2026-08-10T22:43:54.161445Z","title":"MOT16: A benchmark for multi-object tracking,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.161445Z"},"links":{"cited_paper":"/paper/1603.00831","citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:acca35005b5081f1602c2d135049f78248de2896c774f46dc1069953f08b1de0","observation_id":"ec469f38-b90a-4c45-a03a-4fa306b232b5","resolution":{"observed_at":"2026-08-10T22:43:54.161445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.09003","last_updated":"2020-03-19T20:08:24Z","snapshot_observed_at":"2026-08-10T18:10:19.092488Z","submitted_at":"2020-03-19T20:08:24Z","title":"MOT20: A benchmark for multi object tracking in crowded scenes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.09003","snapshot_observed_at":"2026-08-10T22:43:54.165308Z","title":"MOT20: A benchma rk for multi object tracking in crowded scenes,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.165308Z"},"links":{"cited_paper":"/paper/2003.09003","citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:5504af70c6e45a0e8114f9d1a4535fe5170d6aaf0f0e3aaac51f846c180416ef","observation_id":"c1905424-0e7a-4912-ba0a-79311510bd7d","resolution":{"observed_at":"2026-08-10T22:43:54.165308Z","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":"2026-08-10T22:43:54.534742Z","title":"DanceTrack: Multi-object tracking in uniform appearance and diverse motion,","venue":null,"work_id":"63e17c2a-c2ec-4dc0-abb6-737c1ac3f25a","year":2022},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.168928Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:624feefac331ab797a23cc92dc2d4040b7b3f5cc714b1272d34c20005fc1fdd3","observation_id":"dd920264-cefb-4e7e-8b8c-a73f6ae52886","resolution":{"observed_at":"2026-08-10T22:43:54.540078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.516768Z","title":"Tracking objects as points,","venue":null,"work_id":"e8003b47-559d-4aa4-ad66-cbb87505b793","year":2020},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.172090Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:9fc9b01f4420b904cd4fdcb3b2ae4c742c62d1ebac8d734534c53c7013ee87a4","observation_id":"3d71345c-c143-4713-afcb-1b637384ddea","resolution":{"observed_at":"2026-08-10T22:43:54.522102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-10T22:43:54.175943Z","title":"Evaluating multipl e object tracking performance: the CLEAR MOT metrics,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.175943Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:5f0079b63271d75180a980d09fa68e5be792df0bbb4df489a9e5a4116ff7bb91","observation_id":"57ce3229-4dde-406d-a62f-b12bc3ed37da","resolution":{"observed_at":"2026-08-10T22:43:54.175943Z","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":"2026-08-10T22:43:54.500061Z","title":"Performance measures and a data set for multi-target, multi-camera trac king,","venue":null,"work_id":"d32ad8b5-af4a-437a-a6b1-9e37f4976619","year":2016},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.182154Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:d43ddf8097ed04ac80bf1c2de9586391cc7308fb5971877125cb05e5727bdb63","observation_id":"710dbded-c7a0-4201-b715-96ba6ebfd764","resolution":{"observed_at":"2026-08-10T22:43:54.506212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:43:54.485795Z","title":"HOTA: A higher order metric for evaluating multi- object tracking,","venue":null,"work_id":"e5cf187a-f7be-4956-8d17-5aade7355666","year":null},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.186370Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:76a7411618681f0a5738b424b0236183804369453d50be01001a261c70c639e1","observation_id":"b8777bb2-70ee-4975-a0b9-0b502981fe27","resolution":{"observed_at":"2026-08-10T22:43:54.490585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2107.08430","last_updated":"2021-08-06T03:22:14Z","snapshot_observed_at":"2026-07-06T11:30:06.143581Z","submitted_at":"2021-07-18T12:55:11Z","title":"YOLOX: Exceeding YOLO Series in 2021","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.08430","snapshot_observed_at":"2026-08-10T22:43:54.194697Z","title":"YOLOX: Exceeding YOLO series in 2021,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.194697Z"},"links":{"cited_paper":"/paper/2107.08430","citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:6924b56358d0c1e97bcf1f17fe82a825030accd18fa713615cb020b24955ff46","observation_id":"d5dc80cd-d7db-4d80-a119-50c681fad7ee","resolution":{"observed_at":"2026-08-10T22:43:54.194697Z","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":"2026-08-10T22:43:54.190598Z","title":"Available: https://doi.org/10.1007/s11 263-020-01375-2","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","version":3},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T22:43:54.190598Z"},"links":{"citing_paper":"/paper/2501.00843"},"observation_digest":"sha256:f9a50199045a1d94f55a383ca330a0aa8513967f7c671fb8be6b46f8ac9eef7b","observation_id":"f7b9ca04-4e49-4644-acb5-74fd478ab289","resolution":{"observed_at":"2026-08-10T22:43:54.190598Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.00843","last_updated":"2025-05-10T09:03:20Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T19:51:51.460742Z","submitted_at":"2025-01-01T13:51:03Z","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":26},"total_outbound_references":40},"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 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2501.00843."}