{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ","short_pith_number":"pith:TJQ7ZMU7","schema_version":"1.0","canonical_sha256":"9a61fcb29f521dfb3961e67295f6658c29ad364d86e55c320faf0a9435880dfe","source":{"kind":"arxiv","id":"1412.2066","version":2},"attestation_state":"computed","paper":{"title":"Learning Multi-target Tracking with Quadratic Object Interactions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Charless C. Fowlkes, Shaofei Wang","submitted_at":"2014-12-05T17:04:35Z","abstract_excerpt":"We describe a model for multi-target tracking based on associating collections of candidate detections across frames of a video. In order to model pairwise interactions between different tracks, such as suppression of overlapping tracks and contextual cues about co-occurence of different objects, we augment a standard min-cost flow objective with quadratic terms between detection variables. We learn the parameters of this model using structured prediction and a loss function which approximates the multi-target tracking accuracy. We evaluate two different approaches to finding an optimal set of"},"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":"1412.2066","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-12-05T17:04:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"462c9f6f67f4cfbd060c5fa06ca401a2187366c5ba6c7bf870b5005943e85e3f","abstract_canon_sha256":"3196eceae7bf02bef705dbac9c3a05874bef2792f186e0254dcaa6eab1d9642b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:31:46.043311Z","signature_b64":"j7NT2QL2R19mHz9VVgzLOIDMy9IyAzHflJIidf+/o56gDREZ/lTPGRhMmgpMD+JdCeXeF9QaLhoZ+Z6plXu3CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a61fcb29f521dfb3961e67295f6658c29ad364d86e55c320faf0a9435880dfe","last_reissued_at":"2026-05-18T02:31:46.042777Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:31:46.042777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Multi-target Tracking with Quadratic Object Interactions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Charless C. Fowlkes, Shaofei Wang","submitted_at":"2014-12-05T17:04:35Z","abstract_excerpt":"We describe a model for multi-target tracking based on associating collections of candidate detections across frames of a video. In order to model pairwise interactions between different tracks, such as suppression of overlapping tracks and contextual cues about co-occurence of different objects, we augment a standard min-cost flow objective with quadratic terms between detection variables. We learn the parameters of this model using structured prediction and a loss function which approximates the multi-target tracking accuracy. We evaluate two different approaches to finding an optimal set of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.2066","kind":"arxiv","version":2},"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":"1412.2066","created_at":"2026-05-18T02:31:46.042869+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.2066v2","created_at":"2026-05-18T02:31:46.042869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.2066","created_at":"2026-05-18T02:31:46.042869+00:00"},{"alias_kind":"pith_short_12","alias_value":"TJQ7ZMU7KIO7","created_at":"2026-05-18T12:28:49.207871+00:00"},{"alias_kind":"pith_short_16","alias_value":"TJQ7ZMU7KIO7WOLB","created_at":"2026-05-18T12:28:49.207871+00:00"},{"alias_kind":"pith_short_8","alias_value":"TJQ7ZMU7","created_at":"2026-05-18T12:28:49.207871+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/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ","json":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ.json","graph_json":"https://pith.science/api/pith-number/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/graph.json","events_json":"https://pith.science/api/pith-number/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/events.json","paper":"https://pith.science/paper/TJQ7ZMU7"},"agent_actions":{"view_html":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ","download_json":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ.json","view_paper":"https://pith.science/paper/TJQ7ZMU7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.2066&json=true","fetch_graph":"https://pith.science/api/pith-number/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/graph.json","fetch_events":"https://pith.science/api/pith-number/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/action/storage_attestation","attest_author":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/action/author_attestation","sign_citation":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/action/citation_signature","submit_replication":"https://pith.science/pith/TJQ7ZMU7KIO7WOLB4ZZJL5TFRQ/action/replication_record"}},"created_at":"2026-05-18T02:31:46.042869+00:00","updated_at":"2026-05-18T02:31:46.042869+00:00"}