Correlation Filter Based on Saliency Detection and Channel Selection for Visual Object Tracking

To improve the utilization of convolution features by correlation filter trackers and reduce the influence of interference feature channels on algorithm performance, a correlation filter visual object tracking algorithm based on saliency detection and channel selection is proposed in this paper. Firstly, the HOG features and double-layer convolution features are used to represent the target, and the target salient region mask is obtained by the saliency detection method. Secondly, a channel selection mechanism is designed by using the salient region feature energy and the search region feature energy to remove redundant feature channels containing a large number of background information. Extensive evaluation results obtained on the OTB2015 benchmark demonstrate the effectiveness of the proposed method. The success rate and precision of the proposed algorithm are 67.5% and 91.3%, which are 5.4% and 9.1% higher than the benchmark algorithm BACF, respectively. In addition, according to the experimental results, it can be seen that the proposed tracker has a significant improvement in tracking performance compared with competitors in tracking scenarios with challenges such as deformation, background clutter, and rotation.