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O.: Bayesian background modeling for foreground detection

By Fatih Porikli and Oncel TuzelFatih Porikli and Oncel Tuzel


We propose a Bayesian learning method to capture the background statistics of a dynamic scene. We model each pixel as a set of layered normal distributions that compete with each other. Using a recursive Bayesian learning mechanism, we estimate not only the mean and variance but also the probability distribution of the mean and covariance of each model. This learning algorithm preserves the multimodality of the background process and is capable of estimating the number of required layers to represent each pixel

Publisher: ACM
Year: 2005
OAI identifier: oai:CiteSeerX.psu:
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