Efficient adaptive density estimation per image pixel for the task of background subtraction

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    Abstract

    We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms.
    Original languageUndefined
    Pages (from-to)773-780
    Number of pages8
    JournalPattern recognition letters
    Volume27
    Issue number1636734 (P
    DOIs
    Publication statusPublished - 6 Jan 2006

    Keywords

    • Background subtraction On-line density estimation Gaussian mixture model Non-parametric density estimation
    • IR-57717
    • METIS-237970
    • EWI-9308

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