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The recent trend in multiple object tracking (MOT) is jointly solvingdetection and tracking, where object detection and appearance feature (ormotion) are learned simultaneously. Despite competitive performance, in crowdedscenes, joint detection and tracking usually fail to find accurate objectassociations due to missed or false detections. In this paper, we jointly modelcounting, detection and re-identification in an end-to-end framework, namedCountingMOT, tailored for crowded scenes. By imposing mutual object-countconstraints between detection and counting, the CountingMOT tries to find abalance between object detection and crowd density map estimation, which canhelp it to recover missed detections or reject false detections. Our approachis an attempt to bridge the gap of object detection, counting, andre-Identification. This is in contrast to prior MOT methods that either ignorethe crowd density and thus are prone to failure in crowded scenes, or depend onlocal correlations to build a graphical relationship for matching targets. Theproposed MOT tracker can perform online and real-time tracking, and achievesthe state-of-the-art results on public benchmarks MOT16 (MOTA of 79.7), MOT17(MOTA of 81.3%) and MOT20 (MOTA of 78.9%).

CountingMOT: Joint Counting, Detection and Re-Identification for Multiple Object Tracking