A full-detection association tracker with confidence optimization for real-time multi-object tracking

Abstract

Multi-object tracking (MOT) aims to obtain trajectories with unique identifiers for multiple objects in a video stream. In current approaches, confidence thresholds were frequently used to perform multi-stage data association. However, these thresholds could introduce instability into the algorithm when confronted with diverse scenarios. This article proposed confidence-optimization tracker (COTracker), a full-detection association tracker based on confidence optimization. COTracker incorporated detection confidence and matching cost as covariates and modeled tracklet confidence using exponential moving average (EMA). It introduced confidence cues in data association by generating a weighting matrix containing detection and tracklet confidence. Experimental results showed that COTracker achieved 63.0 HOTA and 77.1 IDF1 on MOT17 test set. On the more crowded MOT20, it achieves 62.4 HOTA and 76.1 IDF1. Compared with threshold-based methods, COTracker showcased the ability to handle various complex scenarios without adjusting the confidence threshold. Furthermore, its outstanding tracking speed, meeting the requirements of real-time tracking, positions it with potential value in applications such as unmanned driving and drone tracking. The source codes are available at https://github.com/LiYi199983/CWTracker.