ERMOT: Evidence Reasoning-Based Robust Multiple Object Tracking Method

Xiaobin Xu, Xiaochuang Wang, Fulong Wu, Zehui Zhang, Leilei Chang

Abstract: Multiple object tracking (MOT) is one of the key technologies for intelligent industrial information systems. Confidence fluctuation and identity switch are common occurrences in MOT, which can significantly decrease the tracking performance. To solve the above issues, we propose the evidence reasoning-based robust multiple object tracking method (ER-MOT). First, you only learn one representation (YOLOR) and deep simple online and realtime tracking (SORT) are used to calculate the tracking confidence of each target in the video stream. Then, ER-based dynamic update algorithm is proposed to enhance the robustness of the tracking method, which can convert the tracking confidence of the target into a piece of evidence of the target identity and dynamically update the current evidence by fusing historical evidence. In addition, the forgetting strategy and the simulated annealing algorithm are used to enhance the ER-based dynamic update algorithm performance. The effectiveness of the proposed method is verified on the multiple MOT Challenge datasets. Experimental results demonstrate that the proposed ER-MOT can effectively reduce the occurrences of identity switch and enhance the tracking robustness under disturbance scenarios including target occlusion, unstable video quality, and dynamic target changes.