{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4BIMAETZGEWDSJIKRT4LCPB7VZ","short_pith_number":"pith:4BIMAETZ","schema_version":"1.0","canonical_sha256":"e050c01279312c39250a8cf8b13c3fae49a8d1f52439ae66ec88629e0eaf71b2","source":{"kind":"arxiv","id":"1907.03465","version":1},"attestation_state":"computed","paper":{"title":"A unified neural network for object detection, multiple object tracking and vehicle re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiakui Wang, Yuhao Xu","submitted_at":"2019-07-08T09:07:22Z","abstract_excerpt":"Deep SORT\\cite{wojke2017simple} is a tracking-by-detetion approach to multiple object tracking with a detector and a RE-ID model.\n  Both separately training and inference with the two model is time-comsuming.\n  In this paper, we unify the detector and RE-ID model into an end-to-end network, by adding an additional track branch for tracking in Faster RCNN architecture. With a unified network, we are able to train the whole model end-to-end with multi loss, which has shown much benefit in other recent works.\n  The RE-ID model in Deep SORT needs to use deep CNNs to extract feature map from detect"},"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":"1907.03465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-08T09:07:22Z","cross_cats_sorted":[],"title_canon_sha256":"fcd76394a95d2ebbc82da6898110898956faee079cb57fa3999e43d705e56702","abstract_canon_sha256":"5e7afd80eae0ea03ef7d324746f82366803ed0646b8cdbeac94c47ac0a45d455"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:14.528709Z","signature_b64":"WnYZk8NrKo6gyonTEyiAcyDjig2+/CRLf432bqk2gr+XT4zwepIj7vJum8Jo7dnw1BSYDZTTj3p8lQvYxSOsCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e050c01279312c39250a8cf8b13c3fae49a8d1f52439ae66ec88629e0eaf71b2","last_reissued_at":"2026-05-17T23:41:14.528290Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:14.528290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A unified neural network for object detection, multiple object tracking and vehicle re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiakui Wang, Yuhao Xu","submitted_at":"2019-07-08T09:07:22Z","abstract_excerpt":"Deep SORT\\cite{wojke2017simple} is a tracking-by-detetion approach to multiple object tracking with a detector and a RE-ID model.\n  Both separately training and inference with the two model is time-comsuming.\n  In this paper, we unify the detector and RE-ID model into an end-to-end network, by adding an additional track branch for tracking in Faster RCNN architecture. With a unified network, we are able to train the whole model end-to-end with multi loss, which has shown much benefit in other recent works.\n  The RE-ID model in Deep SORT needs to use deep CNNs to extract feature map from detect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03465","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":"1907.03465","created_at":"2026-05-17T23:41:14.528369+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03465v1","created_at":"2026-05-17T23:41:14.528369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03465","created_at":"2026-05-17T23:41:14.528369+00:00"},{"alias_kind":"pith_short_12","alias_value":"4BIMAETZGEWD","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"4BIMAETZGEWDSJIK","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"4BIMAETZ","created_at":"2026-05-18T12:33:10.108867+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/4BIMAETZGEWDSJIKRT4LCPB7VZ","json":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ.json","graph_json":"https://pith.science/api/pith-number/4BIMAETZGEWDSJIKRT4LCPB7VZ/graph.json","events_json":"https://pith.science/api/pith-number/4BIMAETZGEWDSJIKRT4LCPB7VZ/events.json","paper":"https://pith.science/paper/4BIMAETZ"},"agent_actions":{"view_html":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ","download_json":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ.json","view_paper":"https://pith.science/paper/4BIMAETZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03465&json=true","fetch_graph":"https://pith.science/api/pith-number/4BIMAETZGEWDSJIKRT4LCPB7VZ/graph.json","fetch_events":"https://pith.science/api/pith-number/4BIMAETZGEWDSJIKRT4LCPB7VZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ/action/storage_attestation","attest_author":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ/action/author_attestation","sign_citation":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ/action/citation_signature","submit_replication":"https://pith.science/pith/4BIMAETZGEWDSJIKRT4LCPB7VZ/action/replication_record"}},"created_at":"2026-05-17T23:41:14.528369+00:00","updated_at":"2026-05-17T23:41:14.528369+00:00"}