Background subtraction with Dirichlet processes


Fincham Haines, T. and Xiang, T., 2012. Background subtraction with Dirichlet processes. In: 12th European Conference on Computer Vision,2012, 2012-10-07 - 2012-10-13.

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    Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a per-pixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adjacent pixels. A new method is presented that uses a Dirichlet process Gaussian mixture model to estimate a per-pixel background distribution, which is followed by probabilistic regularisation. Key advantages include inferring the per-pixel mode count, such that it accurately models dynamic backgrounds, and that it updates its model continuously in a principled way.


    Item Type Conference or Workshop Items (Paper)
    CreatorsFincham Haines, T.and Xiang, T.
    DepartmentsFaculty of Science > Computer Science
    Research CentresEPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
    ID Code56737


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