Improved Background Mixture Models 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. This paper proposes an update of the popular Mixture of Gaussian Models technique. Experimental analysis shows a lack of this technique to cope with quick illumination changes. A different matching mechanism is proposed to improve the general robustness and a comparison with related work is given. Finally, experimental results are presented to show the gain of the updated technique, according to the standard scheme and the related techniques.