Online Video Foreground Segmentation using General Gaussian Mixture Modeling

In this paper, we propose a robust video foreground modeling by using a finite mixture model of general Gaussian distributions (GGD). 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 GGDs 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.