The feature expression of the target is the key to the target tracking process. The artificial features are relatively simple and have strong real-time performance, but the expression ability is insufficient. It is easy to produce tracking drift when dealing with the problems of rapid change and target occlusion. The strong feature expression ability of deep neural network (DNN) in target detection and recognition tasks makes DNN gradually become a feature extraction tool. A deeper residual neural network (ResNet) is used to replace VGG-19 network as a feature extraction tool. First, the special additional layer structure and convolution layer features in ResNet-50 are fused to obtain target representation features with stronger robustness. Then, the feature is filtered and the target position is determined according to the maximum response value. Finally, in order to expand the application scene of the algorithm in the field of local target tracking, a graphic based visual saliency detection algorithm is used to increase the weight value of the local target and suppress background information, so as to improve the target representation ability of the feature layer.