The goal 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. However, overlapping between different targets can still lead to identity switch issues. To meet this challenge, we propose unfctrack, which employs unfalsified control to address the identity-switch problem in multi-object tracking. Specifically, we establish sequences of appearance information variations for the trajectories during the tracking process and a detection and rectification module is designed for identity-switch detection and recovery. Additionally, a simple and effective strategy is proposed to address the issue of ambiguous matching of appearance information during the data association process. Extensive experiments are conducted to evaluate the effectiveness of the unfctrack on the public MOT datasets. The results demonstrate that the unfctrack exhibits excellent effectiveness and robustness in handling tracking errors caused by occlusions and rapid movements.