跟踪遮挡目标的一种鲁棒算法

为了解决在跟踪目标过程中的遮挡问题,引入Kalman滤波器为Mean Shift跟踪算法选择初始点,在跟踪稳定的情况下进行模型更新以消除由于目标缓慢变化而产生的累积误差对跟踪结果的影响。根据Kalman滤波器残差的大小判定是否发生遮挡,遮拦检测算法对目标进行分块检测从而把遮挡分为部分遮挡和完全遮挡两种情况,并对两种情况进行区别讨论：对部分遮挡情况不做特殊处理;对完全遮挡情况,结合目标的运动方向提出6点搜索策略来找回目标。实验表明,该算法能很好地解决跟踪运动目标过程中目标的遮挡问题。

Robust algorithm for occlusion problem in tracking

We employ Kalman filter to choose the start point for Mean Shift tracking algorithm and update the target model to avoid the accumulative error result from the tardy change of target.Kalman filter error is exploited to decide whether the occlusion detection algorithm works,which divides the target into four parts and detects respectively.And occlusion is classified into two categories: partial occlusion and complete occlusion,we utilize the velocity of the target to present six-point research strategy to deal with complete occlusion.Experimental results show that the algorithm is robust to solve occlusion problem.