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A Recursive Kernel Density Learning Framework for Robust Foreground Object Segmentation |
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PP: 363-369 |
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Author(s) |
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Qingsong Zhu,
Zhanpeng Zhang,
Yaoqin Xie,
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Abstract |
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| Dynamic video segmentation is an important research topic in computer vision. In this paper, we present a novel recursive
Kernel Density Learning framework based video segmentation method. In the algorithm, local maximum in the density functions is
approximated recursively via a mean shift method firstly. Via a proposed thresholding scheme, components and parameters in the
mixture Gaussian distributions can be selected adaptively, and finally converge to a relative stable background distribution mode. In the
segmentation, foreground is firstly separated by simple background subtraction method. And then, the Bayes classifier is introduced to
eliminate the misclassifications points to improve the segmentation quality. Experiments on a series of typical video clips are used to
compare with some previous algorithms.
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