Data association is an important step in multiple object tracking(MOT), which generally requires identity matching between objects and detections based on feature similarity. Some objects or detections may remain isolated after match is completed, which is the missing phenomenon that may lead to track interruption or identity confusion. Therefore, in order to improve the accuracy and stability of MOT and suppress the missing phenomenon in data association, this paper proposes an anti-missing mechanism based on high-performance single object tracker(SOT) and rematching. The mechanism uses Transformer and diffusion model to design a SOT that meets the requirements of MOT to track missing objects and rematch missing detections by remembering the object information. In this paper, the effect of SOT and rematching methods in anti-missing mechanism is verified by ablation experiments, and the effect of this mechanism on the tracking performance of MOT algorithm is tested on standard datasets. The results show that the performance of all algorithms is improved comprehensively with the addition of this mechanism, which can effectively suppress the missing phenomenon in MOT.