A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling Export

@proceedings{citeulike:1906855, abstract = {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.}, author = {Allili, Mohand S. and Bouguila, Nizar and Ziou, Djemel}, booktitle = {Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on}, citeulike-article-id = {1906855}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4228578}, journal = {Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on}, keywords = {foreground, gmm, illumination, invariant, mixtute, segmentation, video}, pages = {503--509}, posted-at = {2007-11-13 10:56:45}, priority = {5}, title = {A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4228578}, year = {2007} }

TY - CONF ID - citeulike:1906855 L3 - citeulike-article-id:1906855 N2 - 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. JF - Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on EP - 509 TI - A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling T2 - Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on SP - 503 KW - foreground KW - gmm KW - illumination KW - invariant KW - mixtute KW - segmentation KW - video AU - Allili, Mohand AU - Bouguila, Nizar AU - Ziou, Djemel PY - 2007/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4228578 ER -