GTTrack: Gaussian Transformer Tracker for Visual Tracking
Yun Liang, Fumian Long, Q. Li, Dong Wang · 2023
Recently, Transformer based visual object tracking methods have achieved impressive advancements and significantly improved tracking performance. Transformer includes two modules of self-attention and cross-attention for those methods. However, it brings up two problems: first, the self-attention only considers the relative relation between elements when establishing global association, which can not highlight the essential areas of the tracked target. Second, the cross-attention only relies on feature similarity to locate the target, where the interference of similar objects is challenging. In this paper, we propose a new transformer tracking method of GTTrack by defining Gaussian Attention (GA) and Adaptive Focusing Module (AFM). The GA leads into Gaussian prior to generate a semantic template with robust object features, in which Gaussian prior pays more attention to the central region of the tracked target. The AFM calculates the similarity between current frame and the template by combining the appearance features and position features. The position features are defined with an adaptive Gaussian prior according to the target area in the previous frame. The introduction of position features enhances the contrast between the tracked target and the similar objects. Extensive experiments also demonstrate that the GTTrack outperforms many state-of-the-art trackers and achieves leading performance. Code will be available.