Enhancing Siamese Visual Tracking with Background Relations

Existing Siamese network-based trackers rely on stable appearance features extracted from the target object. However, such features might not be available during tracking due to non-digit appearance deformation and severe occlusion, which result in drift problems. In this paper, we propose a background-augmented tracking network that incorporates background information surrounding the target to make up for missing or deformed target features during the matching process. A novel Background Relation Network (BRNet) is designed to effectively encode and match the background information surrounding candidate objects in the search region to help identify the correct target, and thus avoid tracking error. BRNet can complement the base tracker when reliable target features cannot be obtained. Experiments on the OTB, VOT, and UAV123 datasets demonstrate that the proposed method achieves superior performance over existing state-of-the-art methods while maintaining reasonable real-time speed.