Abstract: In the field of multi-target tracking， the problems of target loss， identity exchange and switch are easy to occur under the conditions of complex background， target occlusion， target scale and attitude change. To solve these problems， a multi-target tracking algorithm was proposed based on detection. A human body and face association algorithm based on YOLO was used to classify and detect the position of the current frame objects， and the feature extraction model based on generative adversarial network was proposed to learn the main features and subtle features of the objects. Then the generative adversarial network was used to generate the motion trajectories of multiple targets， and finally the target’s motion and appearance information were merged to obtain the optimal matching of the target tracking results. The experimental results show that the multi-target tracking algorithm proposed is both accurate and robust. Compared with the current algorithms with the least ID switch， the number of ID switch is 65% less and the accuracy is improved by 0.25%.

Key words: multi-target tracking, generative adversarial network, object detection, path prediction, feature fusion