Abstract

We consider the problem of motion detection by background subtraction. An accurate estimation of the background is only possible if we locate the moving ob jects; meanwhile, a correct motion detection is achieved if we have a good available background model. This work pro- poses a new direction in the way such problems are considered. The main idea is to formulate this class of problem as a joint decision-estimation unique step. The goal is to exploit the way two processes interact, even if they are of a dissimilar nature (symbolic-continuous), by means of a recently introduced framework called mixed-state Markov random fields. In this paper, we will describe the theory behind such a novel statisti- cal framework, that subsequently will allows us to formulate the specific joint problem of motion detection and background reconstruction. Ex- periments on real sequences and comparisons with existing methods will give a significant support to our approach. Further implications for video sequence inpainting will be also discussed.