基于策略梯度的目标跟踪方法

针对目标跟踪过程中的遮挡、形变和快速运动等问题，提出基于策略梯度的目标跟踪方法. 该方法利用策略梯度算法训练策略网络. 该策略网络能够根据当前跟踪结果的可靠性进行动作决策，以避免错误的模板更新或者重新检测丢失的目标. 在决策过程中，通过计算加权置信度差值分析当前跟踪结果的鲁棒性和准确性，使得策略网络能够更准确地评估跟踪结果. 在重检测过程中，提出有效的重检测方法，对大量的搜索区域进行过滤，大大提高了搜索效率，利用决策模块检验重检测结果，确保重检测结果的准确性. 利用提出的算法在OTB数据集及LaSOT数据集上进行评估. 实验结果表明，提出的跟踪算法在原算法的基础上提高了2.5%~4.0%的性能.

Visual object tracking based on policy gradient

An object tracking method based on policy gradient was proposed aiming at the problems of occlusion, deformation and fast motion in the process of object tracking. The policy gradient algorithm was used to train the policy network. The policy network can make action decisions founded on the reliability of current tracking results to avoid the incorrect template update or re-detect the missing targets. During the decision-making process, the robustness and accuracy of the current tracking result were both analyzed by calculating the weighted confidence margin, which helped the policy network to evaluate the tracking results more accurately. During the re-detection process, an efficient re-detection method was proposed to filter a large number of searching areas, which greatly improved the search efficiency. The decision-making module was utilized to examine the re-detected result, which ensured the accuracy of the re-detected results. The proposed algorithm was evaluated on OTB dataset and LaSOT dataset. The experimental results show that the proposed tracking algorithm improves performance by 2.5%-4.0% based on the original algorithm.