Scale Adaptive Target Tracking Based on Kernel Correlation Filter and Residual Network

Abstract

In this paper, a scale adaptive target tracking algorithm based on deep residual network and kernel correlation filter is proposed. Although kernel correlation filter is a fast and effective target tracking algorithm, its tracking effect is not ideal in the case of the occlusion, blur and scale change caused by the fast moving target in the real environment. To deal with these problems, corresponding measures are integrated in the proposed new algorithm. Firstly, the structure of the ResNet50 network was adjusted and trained. The deep residual network was used to extract target features and integrate the kernel correlation filter algorithm to carry out adaptive response graph fusion to find the target location. Then, we add the scale estimation module, and use the HOG feature to replace the original deep feature training scale filter. Based on the structure of binary sort tree, binary search is carried out on the scale, and the scale discriminant index is used to judge whether the target has reached the appropriate scale. Experimental results show that our proposed algorithm can achieve scale adaptation of target tracking, and the tracking performance is further improved.