Gibbs distributions and Markov random field model: application on background modeling in video surveillance

Abstract

Recently, backgrounds modeling methods that employ Time-Adaptive, Per Pixel, and Mixture of Gaussians (TAPPMOG) model have become more and more popular owing to their intrinsic appealing properties in video surveillance. Nevertheless, they are not able parse to monitor global changes in the scene, because they model the background as a set of independent pixel processes. In this paper, Gibbs Distributions-Markov Random Field (GDMRF) model is applied to the background modeling, and then the Simulated Annealing algorithm is developed to extract the background from video sequences. Experimental comparison between our methods and a classic pixel-based approach reveals that our proposed method is really effective in recovering from situations of sudden global illumination changes of the background, and can perfectly adapt the object moving in the background.