Aiming at the decreasing of system performance caused by conflicts between detection and re-ID branches in the multiple object tracking method based on the joint detection and embedding paradigm, we design a network for concentrating multi-level semantic information and constructing differentiated feature maps for different branch. This network effectively relieves the vicious competition between detection and re-ID branches for feature information demands. Secondly, a modified association strategy is adopted, which introduces further information from detection branch into the association process. This strategy provides more opportunities for detections to associate while suppressing the long-term damage caused by environmental noise, effectively reducing the occurrence of misassociation and missed association. The experiments show that the method in this paper has great potential compared with the current advanced methods, and achieves the performance of 75.7% MOTA, 73.4% IDF1 and 60.0% HOTA on the MOT17 test set.