Correlation filter has been proven to be an effective tool for a number of
approaches in visual tracking, particularly for seeking a good balance between
tracking accuracy and speed. However, correlation filter based models are
susceptible to wrong updates stemming from inaccurate tracking results. To
date, little effort has been devoted towards handling the correlation filter
update problem. In this paper, we propose a novel approach to address the
correlation filter update problem. In our approach, we update and maintain
multiple correlation filter models in parallel, and we use deep reinforcement
learning for the selection of an optimal correlation filter model among them.
To facilitate the decision process in an efficient manner, we propose a
decision-net to deal target appearance modeling, which is trained through
hundreds of challenging videos using proximal policy optimization and a
lightweight learning network. An exhaustive evaluation of the proposed approach
on the OTB100 and OTB2013 benchmarks show that the approach is effective enough
to achieve the average success rate of 62.3% and the average precision score of
81.2%, both exceeding the performance of traditional correlation filter based
trackers.

通过更新并维护多个相关性滤波器模型，并使用深度强化学习选择最佳相关滤波器模型，在处理目标外观建模的决策过程中利用决策网络，本文提出了一种解决相关滤波器更新问题的新方法，在 OTB100 和 OTB2013 基准测试上得到了较好的表现，达到了 62.3％的平均成功率和 81.2％的平均精度得分，超过了传统相关滤波器的跟踪器的表现。