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The purpose of multi-object tracking (MOT) is to continuously track andidentify objects detected in videos. Currently, most methods for multi-objecttracking model the motion information and combine it with appearanceinformation to determine and track objects. In this paper, unfalsified controlis employed to address the ID-switch problem in multi-object tracking. Weestablish sequences of appearance information variations for the trajectoriesduring the tracking process and design a detection and rectification modulespecifically for ID-switch detection and recovery. We also propose a simple andeffective strategy to address the issue of ambiguous matching of appearanceinformation during the data association process. Experimental results onpublicly available MOT datasets demonstrate that the tracker exhibits excellenteffectiveness and robustness in handling tracking errors caused by occlusionsand rapid movements.

The detection and rectification for identity-switch based on unfalsified control