Multi-Metric Re-Identification for Online Multi-Person Tracking
Hamid Nodehi, Asadollah Shahbahrami · IEEE Transactions on Circuits and Systems for Video Technology · 2021
Multi-person tracking plays a vital role in intelligent video surveillance systems and has attracted researchers’ growing attention in recent years. This paper proposes a tracking-by-detection method to detect and track all existing persons in video sequences. The proposed method re-identifies detected persons in the latest video frame as observed persons in previous frames and thus generates their trajectories. Re-identification of the proposed approach uses a fusion of six distance metrics. Four metrics, i.e., position, scale, distance to estimated position, and tracklet continuity, are derived from two motion-based features, and two metrics, i.e., dominant colors and histogram of oriented gradients, are derived from corresponding appearance-based features. The proposed method performs tracking in two general steps per each frame. In the first step, all persons in the video frame are detected using the state-of-the-art YOLOv3 object detector. In the second step, the re-identification algorithm generates correspondences between detected persons in the latest video frame and observed persons in previous frames, using the distance matrix built up from compound distances between alldetected–observed personpairs. Experimental results show that our simple yet effective approach achieves significant performance in multi-person tracking compared to existing state-of-the-art methods.