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2003
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A Bayesian framework for Gaussian mixture background modeling

10 years 9 months ago
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 ad hoc manners. 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 one dependent, and a set of intuitive and theoretically optimal solutions can be derived. The proposed framework was tested on meeting and traffic videos and compared favorably over well-known algorithms.
Dar-Shyang Lee, Jonathan J. Hull, Berna Erol
Added 24 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2003
Where ICIP
Authors Dar-Shyang Lee, Jonathan J. Hull, Berna Erol
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