  For the purposes of foreground estimation, the true background model is
unavailable in many practical circumstances and needs to be estimated from
cluttered image sequences. We propose a sequential technique for static
background estimation in such conditions, with low computational and memory
requirements. Image sequences are analysed on a block-by-block basis. For each
block location a representative set is maintained which contains distinct
blocks obtained along its temporal line. The background estimation is carried
out in a Markov Random Field framework, where the optimal labelling solution is
computed using iterated conditional modes. The clique potentials are computed
based on the combined frequency response of the candidate block and its
neighbourhood. It is assumed that the most appropriate block results in the
smoothest response, indirectly enforcing the spatial continuity of structures
within a scene. Experiments on real-life surveillance videos demonstrate that
the proposed method obtains considerably better background estimates (both
qualitatively and quantitatively) than median filtering and the recently
proposed "intervals of stable intensity" method. Further experiments on the
Wallflower dataset suggest that the combination of the proposed method with a
foreground segmentation algorithm results in improved foreground segmentation.
