Box Regression-Guided Anchor-free for Robust Visual Tracking

The Siamese tracker-based approach has achieved significant success in recent years. However, these approaches do not consider the different requirements for input feature in classification and regression branches. The regression branch needs feature information slightly larger than the object region, while the classification branch needs to avoid classification failure caused by the introduction of background information. In this paper, we present a novel Box Regression-Guided Anchor-free for Robust Visual Tracking. Firstly, a scale-aware regression module is designed to satisfy the feature requirements of the regression branch, which can capture feature information of various scales. Secondly, regression-guided classification module is applied to aligning the feature between the regression result and correlation feature, thereby avoiding the introduction of background information to classification branch. In addition, the new correlation operation is introduced to gain more superb correlation feature. Comparsion experimental exhibits that the proposed tracker achieves promising results in five challenging benchmark tests, including GOT-10K, OTB-2015, VOT-2018, VOT-2019 and TrackingNet, and run at an average speed of 60 FPS in real-time.