响应和滤波器偏差感知约束的无人机目标跟踪算法

针对无人机视觉跟踪任务中目标外观变化大、视野角度多变问题，提出基于响应和滤波器偏差感知约束的无人机实时目标跟踪算法. 该算法根据视频帧间响应差和滤波器变化的一致性，通过建模前后帧响应差和滤波器的变化，建立基于响应偏差感知和帧间滤波器偏差约束机制的目标函数，学习目标的外观变化和滤波器的帧间变化. 引入辅助变量构建优化函数，采用交替方向乘子法（ADMM）将计算目标问题转化为求相关滤波器和辅助变量的最优解. 采用跟踪准确度和成功率指标，将所提算法与其他9种算法在DTB70、UAV123@10 fps和UAVDT等3个无人机视频数据库上进行对比实验. 实验结果表明，所提算法对遮挡、形变、角度变化等干扰属性均具有良好的鲁棒性，跟踪平均速度达到39.0帧/s，能够有效跟踪无人机目标.

UAV object tracking algorithm based on response and filter deviation-aware regularization

A real-time unmanned aerial vehicle (UAV) object tracking algorithm based on the response and filter deviation-aware regularization was proposed, aiming at the problem that targets were easily subject to the huge variation of appearance and various change of viewpoint interference in the UAV sequences. According to the consistency of response and correlation filter difference between video frames, the variation of correlation response and filter difference were modeled. Furthermore, an objective function with constraint scheme was constructed, which can learn variation of object appearance and filter. Meanwhile, an auxiliary variable based on the response and filter deviation-aware regularization was introduced to build an optimization function and alternating direction method of multipliers (ADMM) was used to optimize the solution of the correlation filter and auxiliary variable. To validate the effectiveness of the proposed algorithm, comparison experiments with other 9 algorithms were performed on three UAV tracking benchmarks, including DTB70、UAV123@10 fps and UAVDT, in terms of precision and success rate. Experimental results show that the proposed algorithm has good robustness for occlusion, deformation and view variation and can effectively track the target with an average speed of 39.0 frames of second.