Joint Re-Detection and Re-Identification for Multi-Object Tracking

Within the tracking-by-detection framework, multi-object tracking (MOT) has always been plagued by missing detection. To address this problem, existing methods usually predict new positions of the trajectories first to provide more candidate bounding boxes (BBoxes), and then use non-maximum suppression (NMS) to eliminate the redundant BBoxes. However, when two BBoxes belonging to different objects have a significant intersection over union (IoU) due to occlusion, NMS will mistakenly filter out the one with lower confidence score, and these methods ignore the missing detection caused by NMS. We propose a joint re-detection and re-identification tracker (JDI) for MOT, consisting of two components, trajectory re-detection and NMS with re-identification (ReID). Specifically, the trajectory re-detection could predict new position of the trajectory in detection, a more reliable way than motion model (MM), based on feature matching. Furthermore, we propose to embed ReID features into NMS and take the similarity of the ReID features as an additional necessary condition to determine whether two BBoxes are the same object. Based on the "overlap degree" calculated by IoU and the similarity of ReID features, accurate filtering can be achieved through double-checking. We demonstrate the effectiveness of our tracking components with ablative experiments and surpass the state-of-the-art methods on the three tracking benchmarks MOT16, MOT17, and MOT20.