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Robust foreground object segmentation via background modelling is a difficultproblem in cluttered environments, where obtaining a clear view of thebackground to model is almost impossible. In this paper, we propose a methodcapable of robustly estimating the background and detecting regions of interestin such environments. In particular, we propose to extend the backgroundinitialisation component of a recent patch-based foreground detection algorithmwith an elaborate technique based on Markov Random Fields, where the optimallabelling solution is computed using iterated conditional modes. Rather thanrelying purely on local temporal statistics, the proposed technique takes intoaccount the spatial continuity of the entire background. Experiments withseveral tracking algorithms on the CAVIAR dataset indicate that the proposedmethod leads to considerable improvements in object tracking accuracy, whencompared to methods based on Gaussian mixture models and feature histograms.

MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos