双重响应异常约束的无人机目标跟踪

UAV target tracking with dual response anomaly constraint

针对无人机跟踪目标易受视角变化、部分遮挡、背景杂波等因素影响而难以完成对目标的鲁棒跟踪问题,提出一种双重响应异常约束的无人机目标跟踪算法。首先,通过计算局部响应变化向量和全局响应峰值旁瓣比,在线动态更新空间正则化参数,降低边界效应的影响;其次,针对STRCF算法中的时间正则项的参数固定的问题,提出响应异常峰值判断机制,计算次峰响应系数,并将其融合到时间正则化参数上进行实时更新,避免目标模板变异造成跟踪漂移问题;目标函数通过交替方向乘子法迭代求解最优解。在UAV123、UAV20L和DTB70三个无人机数据集上与代表性的目标跟踪算法进行大量实验,结果表明,在UAV123数据集上的精确度达到71.7%,在DTB70数据集上的跟踪平均速度达到48.3帧/s。本文结果与现有相关滤波类无人机目标跟踪算法相比表现较好,具有较高的实时性和鲁棒性。

To solve the problem that UAV target tracking is difficult to achieve robust tracking due to its vulnerability to perspective changes,partial occlusion,background clutter and other factors,a UAV target tracking algorithm with double response anomaly constraints is proposed.Firstly,by calculating the local response change vector and global response peak side lobe ratio,the algorithm dynamically updates the spatial regularization parameters online to reduce the influence of boundary effect.Secondly,aiming at the problem that the parameters of the time regularization term in the STRCF algorithm are fixed,the judgment mechanism of abnormal response peak is proposed,the sub-peak response coefficient is calculated,and it is fused into the time regularization parameter for real-time update,so as to avoid the tracking drift problem caused by the target template variation.The objective function is iteratively solved by alternate direction multiplier method.A large number of experiments are conducted on UAV123,UAV20L and DTB70 UAV data sets and with representative target tracking algorithms.The results show that the accuracy of UAV123 data set reaches 71.7%,and the average tracking speed of DTB70 data set reaches 48.3 frame/s.Compared with the existing UAV target tracking algorithms based on correlation filtering,the algorithm proposed in this paper has better performance,high real-time performance and robustness,and can better complete UAV target tracking.