{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:53CAYR2IGUPMZPCZ75LDUHWVR4","short_pith_number":"pith:53CAYR2I","schema_version":"1.0","canonical_sha256":"eec40c4748351eccbc59ff563a1ed58f0c2d5d86460e2f8776370d488a54c1ed","source":{"kind":"arxiv","id":"1706.06196","version":1},"attestation_state":"computed","paper":{"title":"Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea Prati, Eyasu Zemene, Marcello Pelillo, Mubarak Shah, Yonatan Tariku Tesfaye","submitted_at":"2017-06-19T22:34:52Z","abstract_excerpt":"In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to sol"},"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":"1706.06196","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-19T22:34:52Z","cross_cats_sorted":[],"title_canon_sha256":"41d99efe9985f593d114edaeec6bc545ca98e1a470315165df63c4f4b6df842c","abstract_canon_sha256":"d81685d4ba0632adc1e6324644ce016f7c7f8bd34cb4604201e357c5cd8d78dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:03.984423Z","signature_b64":"/0XJrMocf+fQw9uEdmqNNWhV9juhkhJITioqa6weekzBzCsD6eJSEqnaV/GrxsyownWJ23sYh4F72Jxx4yDBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eec40c4748351eccbc59ff563a1ed58f0c2d5d86460e2f8776370d488a54c1ed","last_reissued_at":"2026-05-18T00:42:03.983636Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:03.983636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea Prati, Eyasu Zemene, Marcello Pelillo, Mubarak Shah, Yonatan Tariku Tesfaye","submitted_at":"2017-06-19T22:34:52Z","abstract_excerpt":"In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to sol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06196","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":"1706.06196","created_at":"2026-05-18T00:42:03.983775+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.06196v1","created_at":"2026-05-18T00:42:03.983775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06196","created_at":"2026-05-18T00:42:03.983775+00:00"},{"alias_kind":"pith_short_12","alias_value":"53CAYR2IGUPM","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"53CAYR2IGUPMZPCZ","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"53CAYR2I","created_at":"2026-05-18T12:31:00.734936+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09245","citing_title":"CalibFree: Self-Supervised View Feature Separation for Calibration-Free Multi-Camera Multi-Object Tracking","ref_index":55,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4","json":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4.json","graph_json":"https://pith.science/api/pith-number/53CAYR2IGUPMZPCZ75LDUHWVR4/graph.json","events_json":"https://pith.science/api/pith-number/53CAYR2IGUPMZPCZ75LDUHWVR4/events.json","paper":"https://pith.science/paper/53CAYR2I"},"agent_actions":{"view_html":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4","download_json":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4.json","view_paper":"https://pith.science/paper/53CAYR2I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.06196&json=true","fetch_graph":"https://pith.science/api/pith-number/53CAYR2IGUPMZPCZ75LDUHWVR4/graph.json","fetch_events":"https://pith.science/api/pith-number/53CAYR2IGUPMZPCZ75LDUHWVR4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4/action/storage_attestation","attest_author":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4/action/author_attestation","sign_citation":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4/action/citation_signature","submit_replication":"https://pith.science/pith/53CAYR2IGUPMZPCZ75LDUHWVR4/action/replication_record"}},"created_at":"2026-05-18T00:42:03.983775+00:00","updated_at":"2026-05-18T00:42:03.983775+00:00"}