  The purpose of multi-object tracking (MOT) is to continuously track and
identify objects detected in videos. Currently, most methods for multi-object
tracking model the motion information and combine it with appearance
information to determine and track objects. In this paper, unfalsified control
is employed to address the ID-switch problem in multi-object tracking. We
establish sequences of appearance information variations for the trajectories
during the tracking process and design a detection and rectification module
specifically for ID-switch detection and recovery. We also propose a simple and
effective strategy to address the issue of ambiguous matching of appearance
information during the data association process. Experimental results on
publicly available MOT datasets demonstrate that the tracker exhibits excellent
effectiveness and robustness in handling tracking errors caused by occlusions
and rapid movements.
