{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:3BC7USEDV7LRDMKZI2ZT57C2HK","short_pith_number":"pith:3BC7USED","schema_version":"1.0","canonical_sha256":"d845fa4883afd711b15946b33efc5a3a99b92c410c78b868940dcddda8797662","source":{"kind":"arxiv","id":"1603.08592","version":1},"attestation_state":"computed","paper":{"title":"Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bor-Jeng Chen, Gerard Medioni","submitted_at":"2016-03-28T23:47:25Z","abstract_excerpt":"Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view. Most approaches aim to associate detections from frame differencing into tracks. However, slow or stopped vehicles result in long-term missing detections and further cause tracking discontinuities. Relying merely on appearance clue to recover missing detections is difficult as targets are extremely small and in grayscale. In this paper, we address the limitations of detection association methods by coupling it with a local context tracker (LCT), which does not rely on motion det"},"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":"1603.08592","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-28T23:47:25Z","cross_cats_sorted":[],"title_canon_sha256":"4f38a22a0f0615b056fd768d8c6b7ba5cde359a1012d5e9d5330f59c2b22f7c1","abstract_canon_sha256":"518195b2c656d34d6ad4bef7d7b3504f76eceb3793c3f6b2081ccba19170e2f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:05.952764Z","signature_b64":"wI1460WHFymKU/riKlFVW+xzb+YSTNfOA9p0iVEWYsjASJR1Iy/7Bu9PmlshDs3FzX3dO+CfO7LWikln5SOtCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d845fa4883afd711b15946b33efc5a3a99b92c410c78b868940dcddda8797662","last_reissued_at":"2026-05-18T01:18:05.952082Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:05.952082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bor-Jeng Chen, Gerard Medioni","submitted_at":"2016-03-28T23:47:25Z","abstract_excerpt":"Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view. Most approaches aim to associate detections from frame differencing into tracks. However, slow or stopped vehicles result in long-term missing detections and further cause tracking discontinuities. Relying merely on appearance clue to recover missing detections is difficult as targets are extremely small and in grayscale. In this paper, we address the limitations of detection association methods by coupling it with a local context tracker (LCT), which does not rely on motion det"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08592","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":"1603.08592","created_at":"2026-05-18T01:18:05.952187+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.08592v1","created_at":"2026-05-18T01:18:05.952187+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08592","created_at":"2026-05-18T01:18:05.952187+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BC7USEDV7LR","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BC7USEDV7LRDMKZ","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BC7USED","created_at":"2026-05-18T12:29:55.572404+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/3BC7USEDV7LRDMKZI2ZT57C2HK","json":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK.json","graph_json":"https://pith.science/api/pith-number/3BC7USEDV7LRDMKZI2ZT57C2HK/graph.json","events_json":"https://pith.science/api/pith-number/3BC7USEDV7LRDMKZI2ZT57C2HK/events.json","paper":"https://pith.science/paper/3BC7USED"},"agent_actions":{"view_html":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK","download_json":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK.json","view_paper":"https://pith.science/paper/3BC7USED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.08592&json=true","fetch_graph":"https://pith.science/api/pith-number/3BC7USEDV7LRDMKZI2ZT57C2HK/graph.json","fetch_events":"https://pith.science/api/pith-number/3BC7USEDV7LRDMKZI2ZT57C2HK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK/action/storage_attestation","attest_author":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK/action/author_attestation","sign_citation":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK/action/citation_signature","submit_replication":"https://pith.science/pith/3BC7USEDV7LRDMKZI2ZT57C2HK/action/replication_record"}},"created_at":"2026-05-18T01:18:05.952187+00:00","updated_at":"2026-05-18T01:18:05.952187+00:00"}