Abstract: In the traditional target tracking algorithm, when the target is disturbed by various factors such as occlusion and light intensity changes, the correlation filter template updates incorrectly and the error accumulates frame by frame, eventually causing the target tracking failure. Therefore, this paper proposes a robust object tracking algorithm based on VGG network. Firstly, the VGG network is used to extract the average feature map of the local context area image to establish a correlation filter template in the first frame of the input image. Secondly, the VGG network is used to extract the average feature map and the affine transformation average feature map of the local context area image in the subsequent frame of the input image. Thirdly, combining the kernel correlation filter tracking algorithm, the target position and the final target position are adaptively determined. Finally, the algorithm adaptively updates the final average feature map and the final correlation filter template in the current frame of the input image. Experimental results show that the proposed algorithm still has high target tracking accuracy and robustness when the target is disturbed by various factors such as occlusion and light intensity changes.

Key words: target tracking, Visual Geometry Group (VGG) network, kernel correlation filter, feature map update, template update