Multi-template Correlation Filter Tracking Based on Deep Feature

In view of the problem that tracking algorithm may easily fail in realistic scene due to background clutter, occlusion, scale change, target deformation, etc., a multi-template correlation filter tracking algorithm fusing deep feature is proposed. Firstly, the deep feature and Color Name feature of images or image regions are extracted separately. The parameters of multiple templates are learned by kernel correlation filters. Then the kernel correlation filter tracking algorithm is used to obtain the response sets under the two kinds of features. The final target position is obtained by weighted fusion of the response in response sets. Finally, in order to achieve the adaptive scale target tracking, Bayesian statistics is utilized to estimate the optimal target scale and update parameters of kernelized correlation filter simultaneously by maximizing the posterior. Comparative experiments are conducted on the OTB2013 and OTB2015 benchmark databases, involving comparisons of 6 excellent tracking algorithms in the current. The results show that the proposed algorithm has the best performance. The success rate OP(AUE) on the two databases exceeds the KCF algorithm by 10.7% and 12.4% respectively.