SiamTAR: Enhancing Object Tracking With Joint Template Updating and Relocation Mechanisms

Siamese network-based trackers have demonstrated competitive performance in the domain of single object tracking. However, their effectiveness is significantly hindered when the target undergoes challenges such as deformation and illumination changes, due to the fixed template features from the first frame. To address this issue, we propose a novel tracking framework called SiamTAR. This framework adaptively updates the current frame’s template by fusing template features from the first frame with updated template features and tracking box features from the previous frame, thereby effectively improving tracking accuracy. Additionally, to reduce the tracker’s attention to redundant information such as similar shapes, colors, and textures near the target, we designed a feature refinement module. This module integrates three attention mechanisms through two parallel branches to capture critical target information, allowing the tracker to ignore some redundant information. To tackle issues of tracking box drift and inaccurate scale estimation during online tracking, we introduce a relocation mechanism. This mechanism corrects the tracking box position by merging the output tracking box features with the template features. Extensive experiments on multiple datasets validate the superior tracking performance of SiamTAR. Specifically, on the GOT-10K dataset, SiamTAR surpasses the current leading Siamese tracker, SiamPW-RBO, by 1.5% in AO and 7.5% in $SR_{0.75}$ metrics, achieving a tracking speed of 26.23 FPS. Source code is available at https://github.com/rkj12345/SiamTAR.