空间异常适应性的目标跟踪

Spatially Abnormal Adaptive Target Tracking

针对基于空间正则化的相关滤波算法无法精准控制背景区域权重,同时存在由遮挡、形变等因素导致的异常情况会降低跟踪结果可信度的问题,文中提出空间异常适应性的目标跟踪算法.首先,构造自适应空间正则项,使用显著性检测对其权重进行初始化,实现空间适应性.然后,利用交替方向乘子法,降低算法复杂度.最后,设置置信分数,计算检测结果的可信度并分析异常情况,实现异常适应性.在4个公开数据集上的实验表明,文中算法在形变、遮挡、光照变化等多种复杂场景下跟踪效果较优,可基本满足实时性需求.

The correlation filtering algorithm based on spatial regularization cannot suppress the weight of the background region precisely.The credibility of results is reduced by the factors like occlusion and deformation.A spatially abnormal adaptive target tracking is proposed to solve the problems.Firstly,an adaptive spatial regularization term is introduced,and its weight is initialized by significance detection to realize the spatial adaptability.Secondly,the alternating direction method of multipliers is adopted to reduce the complexity of the algorithm.Finally,a verification score is set in each subsequent frame to calculate the reliability of detection result and analyze the abnormal situation.A dynamic update rate for the target model is set and thus the abnormal adaptability is realized.Experiments on four public datasets show that the proposed algorithm produces a good tracking result in different complex scenes,such as deformation,occlusion and illumination variation,and it basically meets the real-time requirements.