The detection and rectification for identity-switch based on unfalsified control
This work addresses tracking errors for video analysis applications, but it appears incremental as it builds on existing motion and appearance modeling methods.
The paper tackled the ID-switch problem in multi-object tracking by using unfalsified control to detect and rectify identity switches, resulting in a tracker that demonstrated excellent effectiveness and robustness on public MOT datasets.
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.