Using Modified Mixture of Gaussians for Background Modeling In Video Surveillance

Maintaining a reference background resides at the heart of any video surveillance system. Dynamic background presents impediment in the establishment of an accurate background model for video surveillance. Existing approaches utilize both statistical and non-statistical techniques for maintaining an approximation of the background. Statistical methods are computationally intensive but produce accurate results. Mixture of Caussians is an efficient adaptive technique for background modeling. Different variants of the techniques are given in literature. In this research study, various novel approaches were proposed and employed for background representation through mixture of Gaussian models. Subtle improvement in foreground detection is reported in some specific cases.