Aiming at the shortcomings of current detection-based multi-target tracking paradigm, a research is conducted based on the algorithm of DeepSort to address the issue of frequent switching of targeted ID resulting from occlusion in tracking process. Firstly,focus should be placed on improving appearance model. Efforts should be made in replacing broadband and residual networks with ResNeXt networks, which introduces the mechanism for convolution attention into the backbone network and establish a new person re-identification network. In doing so, the model can pay more attention to critical information of targets and obtain effective features. Then, YOLOv5 serves as a detection algorithm. Adding detection layer enables the model to respond to targets of different sizes. Moreover, the mechanism for coordinate attention is introduced into the backbone networks. These efforts can further improve the accuracy of detection model. The multi-target tracking experiment is carried out on data sets of MOT16, the multi-target tracking accuracy rate is up to 66.2%, and the multi-target tracking precision ratio is up to 80.8%. All these can meet the needs of real-time tracking.