  Robust foreground object segmentation via background modelling is a difficult
problem in cluttered environments, where obtaining a clear view of the
background to model is almost impossible. In this paper, we propose a method
capable of robustly estimating the background and detecting regions of interest
in such environments. In particular, we propose to extend the background
initialisation component of a recent patch-based foreground detection algorithm
with an elaborate technique based on Markov Random Fields, where the optimal
labelling solution is computed using iterated conditional modes. Rather than
relying purely on local temporal statistics, the proposed technique takes into
account the spatial continuity of the entire background. Experiments with
several tracking algorithms on the CAVIAR dataset indicate that the proposed
method leads to considerable improvements in object tracking accuracy, when
compared to methods based on Gaussian mixture models and feature histograms.
