Multi-object tracking (MOT) is an important research topic in the field of computer vision, including object detection and data association. However, problems such as missed detection and trajectory mismatch often lead to missing target information, thus resulting in missed target tracking and trajectory fragmentation. Uniform tracking confidence is also not conducive to the full utilization of detection results. Considering these problems, we first propose a threshold separation strategy, which sets different tracking thresholds for similarity matching and intersection over union (IoU) matching during association to make the distribution of detection information more reasonable. Then, the missing trajectories are screened and compensated with the predicted trajectories to improve the long-term tracking ability of the algorithm. When applied to different association algorithms or tracking algorithms, a better improvement effect can be obtained. It can achieve high tracking speed while achieving high tracking accuracy on the MOT Challenge dataset.