Spatio-temporal Gaussian Mixture Model for Background Modeling

Abstract

Background subtraction is widely employed in the detection of moving objects when background does not show much dynamic behavior. Many background models have been proposed by researchers. Most of them analyses only temporal behavior of pixels and ignores spatial relations of neighborhood that may be a key to better separation of foreground from background when background has dynamic activities. To remedy, some researchers proposed spatio-temporal approaches usually in the block-based framework. Two recent reviews[1, 2] showed that temporal kernel density estimation(KDE) method and temporal Gaussian mixture model(GMM) perform about equally best among possible temporal background models. Spatio-temporal version of KDE was proposed. However, for GMM, explicit extension to spatio-temporal domain is not easily seen in the literature. In this paper, we propose an extension of GMM from temporal domain to spatio-temporal domain. We applied the methods to well known test sequences and found that the proposed outperforms the temporal GMM.