Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds
Multi-person tracking is often solved with a tracking-by-detection approach that matches all tracks and detections simultaneously based on a distance matrix. In crowded scenes, ambiguous situations with similar track-detection distances occur, which leads to wrong assignments. To mitigate this problem, we propose a new association method that separately treats such difficult situations by modelling ambiguous assignments based on the differences in the distance matrix. Depending on the numbers of tracks and detections, for which the assignment task is determined ambiguous, different strategies to resolve these ambiguous situations are proposed. To further enhance the performance of our tracking framework, we introduce a camera motion-aware interpolation technique and make an adaptation to the motion model, which improves identity preservation. The effectiveness of our approach is demonstrated through extensive ablative experiments with different detection models. Moreover, the superiority w.r.t. other trackers is shown on the challenging MOT17 and MOT20 datasets, where state-of-the-art results are obtained.