A Background Foreground Competitive Model for Background Subtraction in Dynamic Background

Background subtraction is used to remove the relatively motionless background information from video frames and to detect moving objects. Several methods have been proposed for nonparametric modelling of the background. In this paper a novel method for simultaneous non parametric modelling of background and foreground is proposed, which is utilized for classification of pixels as foreground or background, in a competitive manner. Selective updating of the background and foreground models is employed to accommodate changes in the background. Both temporal and spatial dependencies of pixels are utilized in the model updating. The proposed method gives higher Percentage of Correct Classification (PCC) score in dynamic background compared to the other methods, which is verified using standard databases.