Robust spatio-temporal multimodal background subtraction for video surveillance

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 derived from the popular mixture of Gaussian models technique (MGM). We discard the Gaussian assumptions and use models existing of an average and an upper and lower threshold. Additionally, we include a maximum difference with the previous value and present an intensity allowance to cope with gradual lighting changes and photon noise, respectively. Moreover, edge-based image segmentation is introduced to improve the results of the proposed technique. This combination of temporal and spatial information results in a robust object detection technique that deals with several difficult situations. Experimental analysis shows that our system is more robust than MGM and more recent techniques, resulting in less false positives and negatives. Finally, a comparison of processing speed shows that our system can process frames up to 50% faster.