{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:F5TWEFXGNWELZCH2EG7VXJCKVN","short_pith_number":"pith:F5TWEFXG","schema_version":"1.0","canonical_sha256":"2f676216e66d88bc88fa21bf5ba44aab41fdce6d0aa19b930a9fb6b6c32f85e1","source":{"kind":"arxiv","id":"1502.00377","version":1},"attestation_state":"computed","paper":{"title":"Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liang Lin, Xiaowu Chen, Yan Pan, Yongyi Lu","submitted_at":"2015-02-02T06:52:47Z","abstract_excerpt":"In order to track the moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. Instead of the traditional frame-by-frame tracking, our method recovers target trajectories based on a short sequence of video frames, e.g. $15$ frames. We initially calculate a foreground map at each frame, as obtained from a state-of-the-art background model. An attribute graph is then extracted from the foreground map, where the graph vertices are image primitives represented by the composite features. With this gr"},"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":"1502.00377","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-02T06:52:47Z","cross_cats_sorted":[],"title_canon_sha256":"ff6504ba072f402c5936ee380108bafb921326b7372d72ce4b12c5fa582a8977","abstract_canon_sha256":"4cb560f36c19a521a225a614b1e13a750fd7d8b2e4e845e126f1c06257449258"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:28:06.111251Z","signature_b64":"rX2KJ8vQMAkSQ4m83GICMzNW9Ve5S46tNKj+N0sBRiuSRnlVjHJXNr1ROvQMWDswgw3FkTsG/m2rxsG98GwlCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f676216e66d88bc88fa21bf5ba44aab41fdce6d0aa19b930a9fb6b6c32f85e1","last_reissued_at":"2026-05-18T02:28:06.110791Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:28:06.110791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liang Lin, Xiaowu Chen, Yan Pan, Yongyi Lu","submitted_at":"2015-02-02T06:52:47Z","abstract_excerpt":"In order to track the moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. Instead of the traditional frame-by-frame tracking, our method recovers target trajectories based on a short sequence of video frames, e.g. $15$ frames. We initially calculate a foreground map at each frame, as obtained from a state-of-the-art background model. An attribute graph is then extracted from the foreground map, where the graph vertices are image primitives represented by the composite features. With this gr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.00377","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":"1502.00377","created_at":"2026-05-18T02:28:06.110866+00:00"},{"alias_kind":"arxiv_version","alias_value":"1502.00377v1","created_at":"2026-05-18T02:28:06.110866+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.00377","created_at":"2026-05-18T02:28:06.110866+00:00"},{"alias_kind":"pith_short_12","alias_value":"F5TWEFXGNWEL","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_16","alias_value":"F5TWEFXGNWELZCH2","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_8","alias_value":"F5TWEFXG","created_at":"2026-05-18T12:29:19.899920+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/F5TWEFXGNWELZCH2EG7VXJCKVN","json":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN.json","graph_json":"https://pith.science/api/pith-number/F5TWEFXGNWELZCH2EG7VXJCKVN/graph.json","events_json":"https://pith.science/api/pith-number/F5TWEFXGNWELZCH2EG7VXJCKVN/events.json","paper":"https://pith.science/paper/F5TWEFXG"},"agent_actions":{"view_html":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN","download_json":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN.json","view_paper":"https://pith.science/paper/F5TWEFXG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1502.00377&json=true","fetch_graph":"https://pith.science/api/pith-number/F5TWEFXGNWELZCH2EG7VXJCKVN/graph.json","fetch_events":"https://pith.science/api/pith-number/F5TWEFXGNWELZCH2EG7VXJCKVN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN/action/storage_attestation","attest_author":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN/action/author_attestation","sign_citation":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN/action/citation_signature","submit_replication":"https://pith.science/pith/F5TWEFXGNWELZCH2EG7VXJCKVN/action/replication_record"}},"created_at":"2026-05-18T02:28:06.110866+00:00","updated_at":"2026-05-18T02:28:06.110866+00:00"}