Background subtraction based on nonparametric Bayesian estimation

Yan He; Donghui Wang; Miaoliang Zhu

doi:10.1117/12.896509

8 July 2011 Background subtraction based on nonparametric Bayesian estimation

Yan He, Donghui Wang, Miaoliang Zhu

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 80090G (2011) https://doi.org/10.1117/12.896509
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

Abstract

Background subtraction, the task of separating foreground pixels from background pixels in a video, is an important step in video processing. Comparing with the parametric background modeling methods, nonparametric methods use a model selection criterion to choose the right number of components for each pixel online. We model the background subtraction problem with the Dirichlet process mixture, which constantly adapts both the parameters and the number of components of the mixture to the scene.

Citation Download Citation

Yan He, Donghui Wang, and Miaoliang Zhu "Background subtraction based on nonparametric Bayesian estimation", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80090G (8 July 2011); https://doi.org/10.1117/12.896509

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