AdaptiveTrack: An Environment-Aware and Confidence-Refined Framework for Online Multi-Object Tracking

Abstract

Multi-object tracking (MOT) remains challenging in conditions involving occlusion, small objects, rapid motion, and crowding, wherein the accuracy of detection and the quality of association degrade simultaneously. We propose AdaptiveTrack, an online MOT framework featuring a closed-loop, confidence-aware association and recovery design: CSI-IoU adapts spatial overlap based on confidence and scale, EAMO refines similarity through density, velocity, and scale cues, and DCR updates detection confidence utilizing association context before NMS and assignment. A lightweight continuity module additionally preserves identities during missed detections. On MOT17/MOT20, AdaptiveTrack achieves HOTA 67.33 and 66.73, MOTA 82.55 and 78.30, and IDF1 83.20 and 82.57, operating at 23.5 FPS.

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