Color attention tracking with score matching

Abstract

It is an ordinary practice that deep networks are utilized to extract deep features from RGB images. Typically, the popular trackers adopt pre-trained ResNet as a backbone to extract target features, achieving excellent performance. Moreover, Staple has shown that color statistics have complementary cues, while the combination of color statistics and deep features in a unified deep framework has rarely been reported. Therefore, we employ color statistics to construct color attention maps, which are encoded into the deep network to guide the generation of target-aware feature maps. Additionally, DCF-based trackers have an online update module to dynamically update the tracking model, it is particularly necessary to collect reliable target samples. Hence, we refer to the template matching thought to design a score matching method, which is intended to score the tracked targets, this method has the advantage of considering the target extent. In this paper, we conduct sufficient ablation analyses on the color attention module and score matching method to verify their effectiveness. Furthermore, our approaches are combined into the DCF frameworks to construct two brand-new trackers, and both quantitative and qualitative results demonstrate that our trackers can perform favorably against recent and far more sophisticated trackers on multiple public benchmarks.