Sun Dongyue, Wang Xian, Man Yingjie, Deng Ningdao, Peng Zhaoxin
School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, China.
Front Neurorobot. 2023 Jan 11;16:1094892. doi: 10.3389/fnbot.2022.1094892. eCollection 2022.
In recent years, visual tracking algorithms based on Siamese networks have attracted attention for their desirable balance between speed and accuracy. The performance of such tracking methods relies heavily on target templates. Static templates cannot cope with the adverse effects of target appearance change. The dynamic template method, with a template update mechanism, can adapt to the change in target appearance well, but it also causes new problems, which may lead the template to be polluted by noise. Based on the DaSiamRPN and UpdateNet template update networks, 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 was 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 interference and contamination of the template. The experimental results showed that the robustness and EAO of the proposed method were 23% and 9.0% higher than those of the basic algorithm on the VOT2016 dataset, respectively, and that the precision and success were increased by 0.8 and 0.4% on the OTB100 dataset, respectively. The most comprehensive real-time tracking performance was obtained for the above two large public datasets.
近年来，基于暹罗网络的视觉跟踪算法因其在速度和准确性之间实现了理想的平衡而备受关注。此类跟踪方法的性能在很大程度上依赖于目标模板。静态模板无法应对目标外观变化带来的不利影响。具有模板更新机制的动态模板方法能够很好地适应目标外观的变化，但也引发了新的问题，可能导致模板被噪声污染。本文基于DaSiamRPN和UpdateNet模板更新网络，提出了一种具有“动态 - 静态”双模板融合及动态模板自适应更新的暹罗跟踪器。该新方法结合了用于目标跟踪的静态模板和实时更新的动态模板。在更新动态模板时采用了自适应更新策略，这不仅有助于适应目标外观的变化，还能抑制噪声干扰和模板污染的不利影响。实验结果表明，在VOT2016数据集上，该方法的鲁棒性和EAO分别比基本算法高23%和9.0%，在OTB100数据集上，精度和成功率分别提高了0.8%和0.4%。对于上述两个大型公共数据集，该方法获得了最全面的实时跟踪性能。
