快速运动目标的Mean shift跟踪算法

Algorithm for tracking of fast motion objects with Mean shift

针对Meanshift本身的理论缺陷,提出Meanshift和卡尔曼滤波器相结合的快速目标跟踪算法。利用卡尔曼滤波器来获得每帧Meanshift算法的起始位置,然后再利用Meanshift算法得到跟踪位置。在目标出现大比例阻挡情况时,利用卡尔曼残差的计算来关闭和打开卡尔曼滤波器,此时,目标位置的线性预测替代了卡尔曼的作用。试验证明,本算法可以实现对快速运动目标的跟踪,对阻挡也有很好的鲁棒性。

To improve theoretic limitation of Mean shift, an algorithm for tracking of fast motion objects, which combines Mean shift and Kalman filter, is proposed. At first, the starting position of Mean shift is found with Kalman filter in every frame, and then Mean shift is utilized to track the target position. When severe occlusion appears, filtering residuals is exploited to decide whether the Kalman filter works. At this moment, Kalman filter is replaced by linear prediction of object position. Experimental results show that the proposed algorithm can track fast moving objects successfully and have better robust for occlusion.