A Confidence-Aware Matching Strategy For Generalized Multi-Object Tracking

Multi-object tracking (MOT), a crucial task in computer vision, has broad applicability, and recently, tracking-by-detection-based trackers, which separate the processes of object detection and association, are showing state-of-the-art performance. However, while techniques like feature enhancement and distance measures have been extensively explored, the matching strategy itself remains an area that requires more in-depth study. As a result, many trackers still require manual adjustment of sensitive hyper-parameters for each tracking scenario, limiting their adaptability and robustness in dynamic environments. To address these limitations, we introduce CMTrack, a new tracker featuring a novel confidence-aware matching strategy comprised of three modules: confidence-aware cascade matching (CCM), confidence-aware metric fusion (CMF), and confidence-aware feature update (CFU). Our matching strategy enables the tracker to be a generalized and practical solution for various tracking scenarios within a unified framework while obviating manual calibration of hyper-parameters. The effectiveness of CMTrack is demonstrated through comprehensive assessments of three prominent MOT datasets: MOT17, MOT20, and DanceTrack. Notably, our CMTrack consistently surpasses existing state-of-the-art trackers, showcasing its superior generalization capabilities. The source codes and models are open at https://github.com/kamkyu94/CMTrack.