OATrack: A Quality-Gated Progressive Association Framework with a YOLO Detection Cache for UAV Small-Object Multi-Object Tracking

Unmanned aerial vehicle (UAV) multi-object tracking (MOT) is challenging because small targets produce unstable confidence and localization, while viewpoint changes and short occlusions make association ambiguous. We present OATrack, an integrated association framework tailored to UAV small-object tracking. OATrack combines detection–track pair quality based on detection reliability and current-state geometric consistency, quality-gated track-state protection with a track-specific scale prototype, and three-stage progressive assignment for active-track matching and recently lost-track recovery. A standardized YOLO11m-smallobj detection cache supplies identical detections to all association methods and enables reproducible fixed-input evaluation. On VisDrone-MOT val, OATrack reaches HOTA 38.5847 versus 38.4797 for official Ultralytics ByteTrack while reducing identity switches (IDSW) by 69.2%, track fragmentation by 29.7%, and false positives by 68.8%. On the complete 20-sequence UAVDT test split, OATrack improves HOTA, MOTA, and IDF1 from 41.4345, 7.5162, and 51.4916 to 42.7743, 24.8760, and 53.2829, respectively, and reduces IDSW from 639 to 295. HOTA-component and size/occlusion analyses further demonstrate stronger association consistency and false-positive control across the two UAV benchmarks.