Object tracking using an adaptive Kalman filter combined with mean shift
Object tracking using an adaptive Kalman filter combined with mean shift
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DOI：
10.1117/1.3327281
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发表时间：
2010-02-01
影响因子：
1.3
通讯作者：
Sun, Jiancheng
中科院分区：
文献类型：
--
作者：
Li, Xiaohe;Zhang, Taiyi;Sun, Jiancheng
提出了一种自适应卡尔曼滤波与均值漂移相结合的目标跟踪算法。首先建立卡尔曼滤波的系统模型，然后将卡尔曼滤波预测的目标中心作为MS算法的初始值。将MS的搜索结果作为自适应KF的测量值进行反馈，并利用Bhattacharyya系数自适应地调整KF的估计参数。所提出的方法具有鲁棒性的能力，在某些现实世界的复杂情况下，如移动对象部分或全部消失，由于遮挡，快速移动的对象，和突然变化的速度的移动对象跟踪连续帧中的移动对象。实验结果表明，该跟踪算法具有较好的鲁棒性和实用性. 2010年，美国光学仪器工程师学会（Society of Photo-Optical Instrumentation Engineers）[DOI 10.1117/1.3327281]
An object tracking algorithm using an adaptive Kalman filter (KF) combined with mean shift (MS) is proposed. First, the system model of KF is constructed, then the center of the object predicted by KF is used as the initial value of the MS algorithm. The searching result of MS is fed back as the measurement of the adaptive KF, and the estimate parameters of KF are adjusted by the Bhattacharyya coefficient adaptively. The proposed method has the robust ability to track a moving object in consecutive frames under certain real-world complex situations, such as a moving object disappearing partially or totally due to occlusion, fast moving objects, and sudden changes in velocity of a moving object. The experimental results demonstrate that the proposed tracking algorithm is robust and practical. 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3327281]