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快速运动目标的Mean shift跟踪算法
引用本文:朱胜利,朱善安,李旭超.快速运动目标的Mean shift跟踪算法[J].光电工程,2006,33(5):66-70.
作者姓名:朱胜利  朱善安  李旭超
作者单位:浙江大学,电气学院,浙江,杭州,310027
摘    要:针对Mean shift本身的理论缺陷,提出Mean shift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Mean shift算法的起始位置,然后再利用Mean shift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。

关 键 词:核函数  卡尔曼滤波器  目标跟踪
文章编号:1003-501X(2006)05-0066-05
收稿时间:2005-07-18
修稿时间:2005-09-01

Algorithm for tracking of fast motion objects with Mean shift
ZHU Sheng-li,ZHU Shan-an,LI Xu-chao.Algorithm for tracking of fast motion objects with Mean shift[J].Opto-Electronic Engineering,2006,33(5):66-70.
Authors:ZHU Sheng-li  ZHU Shan-an  LI Xu-chao
Abstract: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.
Keywords:Mean shift
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