Dynamic template update with limited candidates based on relational modeling for transformer tracking

Most single-stream Transformer trackers lack a template updating strategy, relying solely on the first frame's image, leading to model drift with alterations in the target's visual characteristics and surroundings. During template updates, issues like poor quality, excessive updates, or improper timing can degrade tracking performance. To mitigate these obstacles, we propose DTUCTrack, a robust approach using dynamic template updates based on mean ensemble selection of high-confidence scores. This approach effectively adapts to alterations in the target's visual presentation and maintains stable tracking. Additionally, we introduce a mechanism to control the number of high-quality template candidates, avoiding issues of insufficient quality or excessive candidates. Extensive trials indicate that the efficacy of our approach outperforms baselines and attains state-of-the-art effectiveness in challenging GOT-10k and OTB100 datasets while maintaining real-time speeds.