Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey
Thierry Bouwmans, Fida El Baf and Bertrand Vachon
Affiliation: Laboratory of Mathematics Image and Applications (LMIA), Pole Science, Universite de La Rochelle, 17000 La Rochelle, France.
Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson  have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.
Keywords: Background modeling, foreground detection, mixture of gaussians
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