BAM-SORT: border-guided activated matching for online multi-object tracking

Abstract

Multi-object tracking aims at estimating object bounding boxes and identity IDs in videos. Most tracking methods combine a detector and a Kalman filter using the IoU distance as a similarity metric for association matching of the previous trajectories with the current detection box. These methods usually suffer from ID switches and fragmented trajectories in response to congested and frequently occluded scenarios. To solve this problem, in this study, a simple and effective association method is proposed. First, a bottom edge cost matrix is introduced for the utilization of depth information to improve the data association and increase the robustness in the case of occlusion. Second, an asymmetric trajectory classification mechanism is proposed to distinguish the false-postive trajectories, and an activated trajectory matching strategy is introduced to reduce the interference of noise and transient objects in tracking. Finally, the trajectory deletion strategy is improved by introducing the number of trajectory state switches to delete the trajectories caused by spurious high-scoring detection boxes in real time, as a result, the number of fragmented trajectories is also reduced. These innovations achieve excellent performance on various benchmarks, including MOT17, MOT20, and especially DanceTrack where interactions and occlusions are frequent and severe. The code and models are available at https://github.com/djdodsjsjx/BAM-SORT/.