{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YRPNWPIQI3DVRRW57KPPM72KNA","short_pith_number":"pith:YRPNWPIQ","schema_version":"1.0","canonical_sha256":"c45edb3d1046c758c6ddfa9ef67f4a6833cef697b0142230a8d44edda2651b17","source":{"kind":"arxiv","id":"2106.02495","version":1},"attestation_state":"computed","paper":{"title":"ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bowen Li, Changhong Fu, Fangqiang Ding, Fuling Lin, Junjie Ye","submitted_at":"2021-06-04T14:05:24Z","abstract_excerpt":"Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to joint"},"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":"2106.02495","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-06-04T14:05:24Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"d24eee4ec8f47549475c7ca3f8cf630f8dda331ed8f7c6e79a33041a56fa028e","abstract_canon_sha256":"762e1fb5117c43e4770109070170ee99a0be82f0ac530352b0bc08e28ca380a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:46:19.876098Z","signature_b64":"f3LUO+PxdRzCOMgKJ+oQTdW5EH/7HTQZsrd3PJTlb59CinI4XoSJaBH6o4nGZpO5+4dCPcROMmAZgbHEiLbACQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c45edb3d1046c758c6ddfa9ef67f4a6833cef697b0142230a8d44edda2651b17","last_reissued_at":"2026-07-05T02:46:19.875683Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:46:19.875683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bowen Li, Changhong Fu, Fangqiang Ding, Fuling Lin, Junjie Ye","submitted_at":"2021-06-04T14:05:24Z","abstract_excerpt":"Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to joint"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.02495","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.02495/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":"2106.02495","created_at":"2026-07-05T02:46:19.875750+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.02495v1","created_at":"2026-07-05T02:46:19.875750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.02495","created_at":"2026-07-05T02:46:19.875750+00:00"},{"alias_kind":"pith_short_12","alias_value":"YRPNWPIQI3DV","created_at":"2026-07-05T02:46:19.875750+00:00"},{"alias_kind":"pith_short_16","alias_value":"YRPNWPIQI3DVRRW5","created_at":"2026-07-05T02:46:19.875750+00:00"},{"alias_kind":"pith_short_8","alias_value":"YRPNWPIQ","created_at":"2026-07-05T02:46:19.875750+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/YRPNWPIQI3DVRRW57KPPM72KNA","json":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA.json","graph_json":"https://pith.science/api/pith-number/YRPNWPIQI3DVRRW57KPPM72KNA/graph.json","events_json":"https://pith.science/api/pith-number/YRPNWPIQI3DVRRW57KPPM72KNA/events.json","paper":"https://pith.science/paper/YRPNWPIQ"},"agent_actions":{"view_html":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA","download_json":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA.json","view_paper":"https://pith.science/paper/YRPNWPIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.02495&json=true","fetch_graph":"https://pith.science/api/pith-number/YRPNWPIQI3DVRRW57KPPM72KNA/graph.json","fetch_events":"https://pith.science/api/pith-number/YRPNWPIQI3DVRRW57KPPM72KNA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA/action/storage_attestation","attest_author":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA/action/author_attestation","sign_citation":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA/action/citation_signature","submit_replication":"https://pith.science/pith/YRPNWPIQI3DVRRW57KPPM72KNA/action/replication_record"}},"created_at":"2026-07-05T02:46:19.875750+00:00","updated_at":"2026-07-05T02:46:19.875750+00:00"}