Learning the model update with local trusted templates for visual tracking

Abstract

Existing Siamese trackers usually do not update templates or adopt single-updating strategies. However, historical information cannot be effectively utilized when using these strategies, and model drift from complex tracking challenges cannot be addressed. To address this issue, a novel tracking framework that learns the model update with local trusted templates is proposed in this paper. The authors propose a complementary confidence evaluation method to select local trusted templates in a sliding window. This provides high-confidence historical information. The authors also propose a method including linear learning and deep learning to learn to model updates. Different from traditional update strategies, the authors’ method combines non-linear and linear updates to obtain reliable templates with the most abundant historical information, which solves the complex tracking challenges to a certain extent. Finally, the adaptive fusion response maps of the two strategies determine the final tracking based on the confidence evaluation. Experimental results on NFS, UAVDT, UAV123, UAV20L and VOT2016 show that our method performs favourably when compared with current state-of-the-art methods.