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.

翻译：多目标跟踪（MOT）旨在持续跟踪并识别视频中检测到的目标。目前，大多数多目标跟踪方法通过建模运动信息，并将其与外观信息相结合来实现目标的判别与跟踪。本文采用无伪控制方法解决多目标跟踪中的身份切换（ID-switch）问题。我们建立跟踪过程中轨迹外观信息变化的序列，并设计一个专门用于身份切换检测与恢复的检测纠正模块。同时，提出一种简单有效的策略，以解决数据关联过程中外观信息模糊匹配的问题。在公开MOT数据集上的实验结果表明，该跟踪器在处理由遮挡和快速运动引起的跟踪误差时，展现出优异的效果和鲁棒性。