Siamese Tracker with “Dynamic-static” Dual-template Fusion and Dynamic Template Adaptive Update

In recent years, most visual tracking algorithms based on Siamese network have attracted much attention for its desirable balance between speed and accuracy. The performance of such tracking methods relies heavily on target templates, and there will appear some problems in the application whether dynamic or static templates are used. Based on DaSiamRPN and UpdateNet template update network, a Siamese tracker with “dynamic-static” dual-template fusion and dynamic template adaptive update is proposed in this paper. The new method combines a static template and a dynamic template that is updated in real time for object tracking. An adaptive update strategy is adopted when updating the dynamic template, which can not only help adjust to the changes in the object appearance but also suppress the adverse effects of noise interfering and contaminating the template. Experimental results show that the robustness and EAO of the proposed method are 23% and 9.0% higher than the basic algorithm on the VOT2016 dataset respectively and that the precision and success are increased by 0.8% and 0.4% on the OTB100 dataset respectively. The best comprehensive performance is obtained on the above two large public datasets, and the anti-interference capability is significantly improved.