GroupTrack: Multi-Object Tracking by Using Group Motion Patterns

The main challenge of Multi-Object Tracking (MOT) lies in maintaining a distinctive identity for each target in dense crowds or occluded scenarios. Although the existing methods have achieved significantly progress by using robust object detectors or complex association strategies, they cannot effectively solve long-term tracking due to individually motion or appearance modeling for each single target. In this paper, we propose a novel 2D MOT tracker GroupTrack, to learn reliable motion state for each target using group motion patterns. Specifically, for each tracklet, we first choose its neighboring ones to form a group of motion patterns, which can provide informative clues for the motion estimation of the current tracklet. Then, we apply the group motion patterns to perform tracklet prediction and data association. By integrating prior from neighboring motion patterns into the data association process, GroupTrack provides a new paradigm for target motion modeling in extremely crowded and occluded scenarios. Through extensive experiments on the public MOT17 and MOT20 datasets, we demonstrate the effectiveness of our approach in challenging scenarios and show state-of-the-art performance at various MOT metrics.