Mixture models based background subtraction for video surveillance applications

Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a mixture of several models has been proposed in the literature. This paper proposes a novel background subtraction technique based on the popular Mixture of Gaussian Models technique. Moreover edge-based image segmentation is used to improve the results of the proposed technique. Experimental analysis shows that our system outperforms the standard system both in processing speed and detection accuracy.