{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NY6ALQOVW4ZJCYPQ6W2PFFSFBH","short_pith_number":"pith:NY6ALQOV","schema_version":"1.0","canonical_sha256":"6e3c05c1d5b7329161f0f5b4f2964509e9157c1c723ce7bd38ae38e4aab6ba45","source":{"kind":"arxiv","id":"1902.01466","version":1},"attestation_state":"computed","paper":{"title":"TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenge Li, Gregory Dobler, Xin Feng, Yao Wang","submitted_at":"2019-02-04T21:39:17Z","abstract_excerpt":"Object detection and object tracking are usually treated as two separate processes. Significant progress has been made for object detection in 2D images using deep learning networks. The usual tracking-by-detection pipeline for object tracking requires that the object is successfully detected in the first frame and all subsequent frames, and tracking is done by associating detection results. Performing object detection and object tracking through a single network remains a challenging open question. We propose a novel network structure named trackNet that can directly detect a 3D tube enclosin"},"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":"1902.01466","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-04T21:39:17Z","cross_cats_sorted":[],"title_canon_sha256":"23093310e3a978718bd3f2053d20521cf9ce8db57180bf241cc5d0d8a720c3a2","abstract_canon_sha256":"bdacb4021e531f9bc9a00a91c4313ede12d0c7201e5bfbad29b8ce6df4dd218d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:47.633559Z","signature_b64":"xvXyRggi6OD6Swb/VnUrEfOStm489QP7S17eL/vKbZUKcvj0rCfqVr0lYnJqpiHvgWxlbQT2y014b1UyIDg/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e3c05c1d5b7329161f0f5b4f2964509e9157c1c723ce7bd38ae38e4aab6ba45","last_reissued_at":"2026-05-17T23:54:47.633056Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:47.633056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenge Li, Gregory Dobler, Xin Feng, Yao Wang","submitted_at":"2019-02-04T21:39:17Z","abstract_excerpt":"Object detection and object tracking are usually treated as two separate processes. Significant progress has been made for object detection in 2D images using deep learning networks. The usual tracking-by-detection pipeline for object tracking requires that the object is successfully detected in the first frame and all subsequent frames, and tracking is done by associating detection results. Performing object detection and object tracking through a single network remains a challenging open question. We propose a novel network structure named trackNet that can directly detect a 3D tube enclosin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.01466","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":"1902.01466","created_at":"2026-05-17T23:54:47.633137+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.01466v1","created_at":"2026-05-17T23:54:47.633137+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.01466","created_at":"2026-05-17T23:54:47.633137+00:00"},{"alias_kind":"pith_short_12","alias_value":"NY6ALQOVW4ZJ","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"NY6ALQOVW4ZJCYPQ","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"NY6ALQOV","created_at":"2026-05-18T12:33:24.271573+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/NY6ALQOVW4ZJCYPQ6W2PFFSFBH","json":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH.json","graph_json":"https://pith.science/api/pith-number/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/graph.json","events_json":"https://pith.science/api/pith-number/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/events.json","paper":"https://pith.science/paper/NY6ALQOV"},"agent_actions":{"view_html":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH","download_json":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH.json","view_paper":"https://pith.science/paper/NY6ALQOV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.01466&json=true","fetch_graph":"https://pith.science/api/pith-number/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/graph.json","fetch_events":"https://pith.science/api/pith-number/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/action/storage_attestation","attest_author":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/action/author_attestation","sign_citation":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/action/citation_signature","submit_replication":"https://pith.science/pith/NY6ALQOVW4ZJCYPQ6W2PFFSFBH/action/replication_record"}},"created_at":"2026-05-17T23:54:47.633137+00:00","updated_at":"2026-05-17T23:54:47.633137+00:00"}