Background subtraction with Dirichlet processes

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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.
Original languageEnglish
Number of pages15
Publication statusPublished - 2012
Event12th European Conference on Computer Vision,2012 - Florence, Italy
Duration: 7 Oct 201213 Oct 2012


Conference12th European Conference on Computer Vision,2012


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