Adaptive Threshold Control Scheme Based on Detection Confidence Distribution for Data Association in Multi-Object Tracking

Abstract

Tracking-by-detection (TBD) frameworks for multi-object tracking (MOT) commonly employ fixed confidence thresholds for data association, making them sensitive to frame-level variations caused by camera motion, illumination changes, and object occlusions. To address this limitation, this paper proposes an Adaptive Confidence Thresholding (ACT) mechanism that dynamically adjusts high-confidence and low-confidence thresholds according to the statistical distribution of detection confidence scores, without introducing additional neural network modules. The proposed method uses the first quartile of confidence scores as a proxy for frame-level detection reliability and the candidate detection count as an indicator of candidate detection load, enabling adaptive threshold selection for robust data association. As a result, degraded true targets are more effectively preserved under challenging conditions while unnecessary background noise is suppressed in stable scenes. Experiments using two representative TBD trackers compared ACT against fixed-threshold and sequence-tuned baselines. The results demonstrate that ACT achieves performance comparable to the sequence-tuned baselines while maintaining real-time processing speed, without requiring sequence-specific threshold tuning.