The Multi-Object Tracking （MOT） task needs to track multiple objects at the same time and ensures the continuity of object identities. To solve the problems in the current MOT process， such as object occlusion， object ID Switch （IDSW） and object loss， the Transformer-based MOT model was improved， and a multi-object tracking method based on dual-decoder Transformer was proposed. Firstly， a set of trajectories was generated by model initialization in the first frame， and in each frame after the first one， attention was used to establish the association between frames. Secondly， the dual-decoder was used to correct the tracked object information. One decoder was used to detect the objects， and the other one was used to track the objects. Thirdly， the histogram template matching was applied to find the lost objects after completing the tracking. Finally， the Kalman filter was utilized to track and predict the occluded objects， and the occluded results were associated with the newly detected objects to ensure the continuity of the tracking results. In addition， on the basis of TrackFormer， the modeling of apparent statistical characteristics and motion features was added to realize the fusion between different structures. Experimental results on MOT17 dataset show that compared with TrackFormer， the proposed algorithm has the IDentity F1 Score （IDF1） increased by 0.87 percentage points， the Multiple Object Tracking Accuracy （MOTA） increased by 0.41 percentage points， and the IDSW number reduced by 16.3%. The proposed method also achieves good results on MOT16 and MOT20 datasets. Consequently， the proposed method can effectively deal with the object occlusion problem， maintain object identity information， and reduce object identity loss.