Statistical Background Modeling for Foreground Detection: A Survey
Résumé
Background modeling is often used in the context of moving objects detection from static cameras. Numerous methods have been developed over the recent years and the most used are the statistical ones. The purpose of this chapter is to provide a recent survey of these different statistical methods. For this, we have classified them in term of generation following the years of publication and the statistical tools used. We then focus on the first generation methods: Single Gaussian, Mixture of Gaussians, Kernel Density Estimation and Subspace Learning using PCA. These original methods are reminded and then we have classified their different improvements in term of strategies. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.