Covariance Tracking with Forgetting Factor and Random Sampling
Covariance Tracking with Forgetting Factor and Random Sampling
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DOI：
10.1142/s021848851100712x
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Xuguang Zhang;Xiaoli Li;M. Liang;Yanjie Wang
中科院分区：
文献类型：
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作者：
Xuguang Zhang;Xiaoli Li;M. Liang;Yanjie Wang
协方差匹配是一种优秀的目标跟踪算法。本文提出了遗忘因子和随机抽样方法来提高协方差跟踪的鲁棒性和效率。首先，利用基于模糊隶属度函数的遗忘因子对协方差矩阵间的距离函数进行加权，以克服相似目标的干扰。然后采用随机抽样的方法来减少协方差匹配的计算时间，以便于实时目标跟踪。实验结果表明，本文提出的算法能够有效地抑制杂波和遮挡问题，且运算速度快。
Covariance matching is an excellent algorithm of target tracking. In this paper, forgetting factor and random sampling methods are proposed to improve the robustness and efficiency of covariance tracking. First, a distance function between covariance matrixes is weighted by using a forgetting factor based on a fuzzy membership function to overcome the disturbances from similar targets. Then a random sampling method is applied to reduce the computing time in covariance matching and to facilitate real-time object tracking. Experiment results show that the algorithm proposed in this paper can effectively mitigate the clutter and occlusion problems at a high computing speed.