Dynamic update template for visual object tracking

Continuous acquisition of the latest information about the shape of the object allows for more efficient and robust classification, as well as accurate estimation of the target state. However, previous methods have often overlooked this problem and used only the target information from the first frame in tracking. In this paper, we propose three specific and practical guidelines aimed at updating the target state, enabling the development of an anchor-free generic object tracker without requiring any prior knowledge. These guidelines offer a clear path and direction for the development process. Using these guidelines, we develop our Dynamic Update Template (DUT) tracker that includes a template, a dynamic template, and a search branch, ensures unambiguous classification scores, provides estimation quality scores, and multiplies them to obtain the pcore, which serves as the basis for updating the dynamic template. By conducting thorough analyses and ablation studies, we validate the efficacy of our proposed guidelines. Our DUT tracker achieves better performance on challenging benchmarks (LaSOT) without excessive modifications. On the extensive TrackingNet dataset, DUT attains an impressive AUC score of 82.1 while maintaining a swift frame rate exceeding 90 FPS, surpassing the threshold for real-time performance.