基于通道可靠性和异常抑制的目标跟踪算法

Target tracking algorithm based on channel reliability and aberrance repression

针对时空正则目标跟踪算法无法有效利用特征,为了缓解边界效应扩大搜索区域导致的滤波器倾向于从背景中学习的问题,提出基于通道可靠性和异常抑制的目标跟踪算法.构造通道正则项,在训练阶段求解不同特征通道对应的权重,实现对不同特征通道的加权,降低通道冗余并提高定位精度.在目标函数中加入异常抑制正则项,约束当前帧的响应图,实现滤波器模型的平滑约束.利用交替方向乘子法将求解目标问题转化为求滤波器、辅助因子以及通道权重的最优解.将所提算法在OTB2015、TempleColor128以及UAV20L公开数据集测试并与其他跟踪算法进行对比.实验结果表明,所提算法在快速运动、光照变化场景中的跟踪效果稳定,基本满足实时性要求.

A target tracking algorithm based on channel reliability and aberrance repression was proposed,aiming at the problem that the spatial-temporal regularized target tracking algorithm could not effectively use features and the filter was inclined to learn from the background since the algorithm expanded the search area in order to alleviate the boundary effect.The channel regularization term was constructed,and the corresponding weights of different feature channels were solved in the training stage to realize the weighting of different feature channels,which reduced channel redundancy and improved the positioning accuracy.The aberrance repression regularization term was added to the objective function to constrain the response map of the current frame and realize the smooth constraint of the filter model.The alternating direction multiplier method was used to transform the objective problem into the optimal solution of the filter,auxiliary factor and channel weight.The proposed method was tested on public datasets such as OTB2015,TempleColor128 and UAV20L,and compared with other tracking algorithms.Experimental results show that the tracking effect of the proposed algorithm is stable under fast motion as well as illumination variation,and it basically meets the real-time requirements.