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A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling

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3 Author(s)
Mohand Said Allili ; University of Sherbrooke ; Nizar Bouguila ; Djemel Ziou

In this paper, we propose a robust video foreground modeling by using a finite mixture model of generalized Gaussian distributions (GDD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GDDS and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model.

Published in:

Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on

Date of Conference:

28-30 May 2007