A Bayesian framework for Gaussian mixture background modeling

Background subtraction is an essential processing component for many video applications. However, its development has largely been application driven and done in an ad hoc manner. In this paper, we provide a Bayesian formulation of background segmentation based on Gaussian mixture models. We show that the problem consists of two density estimation problems, one application independent and one application dependent, and a set of intuitive and theoretically optimal solutions can be derived for both. The proposed framework was tested on meeting and traffic videos and compared favorably to other well-known algorithms.