{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Y4EVAAQA4GYD7QVYAHOUM27IGI","short_pith_number":"pith:Y4EVAAQA","schema_version":"1.0","canonical_sha256":"c709500200e1b03fc2b801dd466be832074b3c24ad86892d05c0dca7601b1f40","source":{"kind":"arxiv","id":"1907.07045","version":1},"attestation_state":"computed","paper":{"title":"Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Borna Bi\\'cani\\'c, Ivan Markovi\\'c, Ivan Petrovi\\'c, Marin Or\\v{s}i\\'c, Sini\\v{s}a \\v{S}egvi\\'c","submitted_at":"2019-07-16T14:58:37Z","abstract_excerpt":"This paper studies the interplay between kinematics (position and velocity) and appearance cues for establishing correspondences in multi-target pedestrian tracking. We investigate tracking-by-detection approaches based on a deep learning detector, joint integrated probabilistic data association (JIPDA), and appearance-based tracking of deep correspondence embeddings. We first addressed the fixed-camera setup by fine-tuning a convolutional detector for accurate pedestrian detection and combining it with kinematic-only JIPDA. The resulting submission ranked first on the 3DMOT2015 benchmark. How"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1907.07045","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-16T14:58:37Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"2550171c6af91240d456a3eee5892533c1235714ddbd608b819213792ae2f2a8","abstract_canon_sha256":"8b0700bf57bf00e15af357075055026917249b6752cde8047bc69b2cb370247d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:28.264630Z","signature_b64":"ZTv0SvKyscACZYC6B1/mMJsKpruW0bbcW9E9F/NiE2+NTXHGOUlppTJYuJvtG/6i/jwtrNLfXj6muoN3vY7rAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c709500200e1b03fc2b801dd466be832074b3c24ad86892d05c0dca7601b1f40","last_reissued_at":"2026-05-17T23:40:28.264044Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:28.264044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Borna Bi\\'cani\\'c, Ivan Markovi\\'c, Ivan Petrovi\\'c, Marin Or\\v{s}i\\'c, Sini\\v{s}a \\v{S}egvi\\'c","submitted_at":"2019-07-16T14:58:37Z","abstract_excerpt":"This paper studies the interplay between kinematics (position and velocity) and appearance cues for establishing correspondences in multi-target pedestrian tracking. We investigate tracking-by-detection approaches based on a deep learning detector, joint integrated probabilistic data association (JIPDA), and appearance-based tracking of deep correspondence embeddings. We first addressed the fixed-camera setup by fine-tuning a convolutional detector for accurate pedestrian detection and combining it with kinematic-only JIPDA. The resulting submission ranked first on the 3DMOT2015 benchmark. How"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07045","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1907.07045","created_at":"2026-05-17T23:40:28.264119+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.07045v1","created_at":"2026-05-17T23:40:28.264119+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07045","created_at":"2026-05-17T23:40:28.264119+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y4EVAAQA4GYD","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y4EVAAQA4GYD7QVY","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y4EVAAQA","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI","json":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI.json","graph_json":"https://pith.science/api/pith-number/Y4EVAAQA4GYD7QVYAHOUM27IGI/graph.json","events_json":"https://pith.science/api/pith-number/Y4EVAAQA4GYD7QVYAHOUM27IGI/events.json","paper":"https://pith.science/paper/Y4EVAAQA"},"agent_actions":{"view_html":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI","download_json":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI.json","view_paper":"https://pith.science/paper/Y4EVAAQA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.07045&json=true","fetch_graph":"https://pith.science/api/pith-number/Y4EVAAQA4GYD7QVYAHOUM27IGI/graph.json","fetch_events":"https://pith.science/api/pith-number/Y4EVAAQA4GYD7QVYAHOUM27IGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI/action/storage_attestation","attest_author":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI/action/author_attestation","sign_citation":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI/action/citation_signature","submit_replication":"https://pith.science/pith/Y4EVAAQA4GYD7QVYAHOUM27IGI/action/replication_record"}},"created_at":"2026-05-17T23:40:28.264119+00:00","updated_at":"2026-05-17T23:40:28.264119+00:00"}