As an important part of the field of image processing, target tracking is widely used in intelligent video surveillance, military reconnaissance and other fields. However, in the face of the target deformation, occlusion, and other complex application scenarios, the relevant filtering algorithms follow the wrong targets, slowly drift to the background and lack of redetection mechanism when the target reappears after occlusion due to the deficiency of strategies for target and background discrimination and occlusion state judgment, which leads to a substantial decline of tracking performance in practical engineering. According to the above problems, the improved design was carried out. Firstly, in normal tracking, the network optimizer was used to update the multi-layer deep feature extraction network, and the loss function was optimized to improve the discrimination ability of target and background. Secondly, the multiple detection and anti-occlusion optimization mechanism was used to determine the tracker state update mechanism. Finally, the integrated detection, tracking and identification based on deep learning was designed to realize the automatic capture of typical targets before tracking and the recapture positioning of typical targets when the targets reappeared after occlusion. In the experimental analysis, the performance was verified from the aspects of tracking accuracy, visual quantitative loss and algorithm speed. The measured data shows that the adopted method performs well in the above aspects, which is better than that of the ECO algorithm before improvement.