{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QZAM57MMLU63MTYXRKYYRTYYUK","short_pith_number":"pith:QZAM57MM","schema_version":"1.0","canonical_sha256":"8640cefd8c5d3db64f178ab188cf18a298fbcc7085da8d170bc09f1fe633a72c","source":{"kind":"arxiv","id":"2503.17237","version":2},"attestation_state":"computed","paper":{"title":"Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Yu-Hsi Chen","submitted_at":"2025-03-21T15:40:18Z","abstract_excerpt":"Detecting and tracking multiple unmanned aerial vehicles (UAVs) in thermal infrared video is inherently challenging due to low contrast, environmental noise, and small target sizes. This paper provides a straightforward approach to address multi-UAV tracking in thermal infrared video, leveraging recent advances in detection and tracking. Instead of relying on the well-established YOLOv5 with DeepSORT combination, we present a tracking framework built on YOLOv12 and BoT-SORT, enhanced with tailored training and inference strategies. We evaluate our approach following the 4th Anti-UAV Challenge "},"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":"2503.17237","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-21T15:40:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"02e56fde4d18f5f239659e3b6df9bba8703f01655e32685dd95539e8b2e54d83","abstract_canon_sha256":"c08a98f72f246f1b54977236db06e9e95de1c728dc140d4311af7ba08d2fe78e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:05.874340Z","signature_b64":"CAWjEYe2oxubtrQc0t/PEkPDo48j2oV6qn32cS7YATBgVyDtKupW5bTBaWwiRetN6PuC7zTUR/11hX45DBG5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8640cefd8c5d3db64f178ab188cf18a298fbcc7085da8d170bc09f1fe633a72c","last_reissued_at":"2026-07-05T11:38:05.873012Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:05.873012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Yu-Hsi Chen","submitted_at":"2025-03-21T15:40:18Z","abstract_excerpt":"Detecting and tracking multiple unmanned aerial vehicles (UAVs) in thermal infrared video is inherently challenging due to low contrast, environmental noise, and small target sizes. This paper provides a straightforward approach to address multi-UAV tracking in thermal infrared video, leveraging recent advances in detection and tracking. Instead of relying on the well-established YOLOv5 with DeepSORT combination, we present a tracking framework built on YOLOv12 and BoT-SORT, enhanced with tailored training and inference strategies. We evaluate our approach following the 4th Anti-UAV Challenge "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17237","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.17237/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2503.17237","created_at":"2026-07-05T11:38:05.873075+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17237v2","created_at":"2026-07-05T11:38:05.873075+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17237","created_at":"2026-07-05T11:38:05.873075+00:00"},{"alias_kind":"pith_short_12","alias_value":"QZAM57MMLU63","created_at":"2026-07-05T11:38:05.873075+00:00"},{"alias_kind":"pith_short_16","alias_value":"QZAM57MMLU63MTYX","created_at":"2026-07-05T11:38:05.873075+00:00"},{"alias_kind":"pith_short_8","alias_value":"QZAM57MM","created_at":"2026-07-05T11:38:05.873075+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.10006","citing_title":"Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK","json":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK.json","graph_json":"https://pith.science/api/pith-number/QZAM57MMLU63MTYXRKYYRTYYUK/graph.json","events_json":"https://pith.science/api/pith-number/QZAM57MMLU63MTYXRKYYRTYYUK/events.json","paper":"https://pith.science/paper/QZAM57MM"},"agent_actions":{"view_html":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK","download_json":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK.json","view_paper":"https://pith.science/paper/QZAM57MM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17237&json=true","fetch_graph":"https://pith.science/api/pith-number/QZAM57MMLU63MTYXRKYYRTYYUK/graph.json","fetch_events":"https://pith.science/api/pith-number/QZAM57MMLU63MTYXRKYYRTYYUK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK/action/storage_attestation","attest_author":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK/action/author_attestation","sign_citation":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK/action/citation_signature","submit_replication":"https://pith.science/pith/QZAM57MMLU63MTYXRKYYRTYYUK/action/replication_record"}},"created_at":"2026-07-05T11:38:05.873075+00:00","updated_at":"2026-07-05T11:38:05.873075+00:00"}