Response adaptive tracking based on convolution neural network

In order to solve the problem of the occlusion, rotation, fast motion, deformation in target tracking, the paper proposes the response adaptive tracking algorithm based on convolution neural network. First, we extract multi-layer convolutional features of target by using convolution neural network, and gain the multi-template response of the target exploiting particle filter algorithm to adaptively learn the objectives of the expected response. Then, the dual form of the objective function is constructed to solve the multi-template joint optimization problem in order to calculate the optimal filtering parameters of each-layer convolutional features in the multi-template case. Finally, we calculate the multi-layer response by utilizing correlation filter algorithm and calculate the final response map by using the weighted fusion method, and then the proposed algorithm estimates the target position by employing the final response map. In this paper, we use the method of OTB-2013 data set to test the algorithm, experimental results show that the overall success rate and accuracy of the algorithm are 0.884 and 0.915, repectively. The algorithm is better than the traditional correlation filter tracking algorithm in distance precision, success rate and average tracking error, so it has a certain research value.