Rethinking the competition between detection and ReID in Multi-Object Tracking

Due to balanced accuracy and speed, joint learning detection and ReID-based one-shot models have drawn great attention in multi-object tracking(MOT). However, the differences between the above two tasks in the one-shot tracking paradigm are unconsciously overlooked, leading to inferior performance than the two-stage methods. In this paper, we dissect the reasoning process of the aforementioned two tasks. Our analysis reveals that the competition of them inevitably hurts the learning of task-dependent representations, which further impedes the tracking performance. To remedy this issue, we propose a novel cross-correlation network that can effectively impel the separate branches to learn task-dependent representations. Furthermore, we introduce a scale-aware attention network that learns discriminative embeddings to improve the ReID capability. We integrate the delicately designed networks into a one-shot online MOT system, dubbed CSTrack. Without bells and whistles, our model achieves new state-of-the-art performances on MOT16 and MOT17. We will release our code to facilitate further work.