Discriminative correlation filters for visual object tracking with adaptive channel-based sample weights

Abstract

In order to improve the accuracy and robustness of correlation filtering algorithm in more challenging scenarios, a target tracking algorithm based on adaptive channel sample weights is proposed in this paper. Firstly, channel attention modules are added to sample branch and search branch respectively to weight the features of each branch respectively, which can effectively improve the feature expression ability. Secondly, according to the channel weights of the two branches, we learn to fuse the weight information, interact the features of the two branches, and suppress the background information. Finally, the sample learning weights are reassigned according to the channel weights of the history frame to make more efficient use of the background information. In this paper, the OTB100 data set is used to verify the effectiveness of the improved algorithm. The area under the curve is increased by 1.6%, which proves the effectiveness of the improved algorithm.