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. We propose a novel background subtraction technique based on the popular Mixture of Gaussian Models technique. Several additions to the technique are made to reduce the complexity while maintaining the same accuracy. Moreover, we propose to incorporate edge-based spatial segmentation to improve the detection results. Experimental analysis shows that our system outperforms the standard system both in processing speed and detection accuracy. Keywords— Moving Object Detection, Video Surveillance, Background subtraction, Mixture of Gaussian Models