Separating background and foregroundin video based on a nonparametric Bayesian model

Xinghao Ding, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Separating background and foreground in video is a fundamental problem in computer vision. We present a Bayesian hierarchical model to address this challenge, and apply it to video with dynamic scenes. The model uses a nonparametric prior, a beta-bernoulli process, for both the background and foreground representation. Additionally, the model uses neighborhood information of each pixel to encourage group clustering of the foreground. A collapsed Gibbs sampler is used for efficient posterior inference. Experimental results show competitive performance of the proposed model. © 2011 IEEE.
Original languageEnglish (US)
Title of host publicationIEEE Workshop on Statistical Signal Processing Proceedings
Pages321-324
Number of pages4
DOIs
StatePublished - Sep 5 2011
Externally publishedYes

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