Spatial color Mixture Of Gaussians model (SMOG model) based similarity measure is superior to the popular
color histogram based one since it considers not only the colors in a region, but also the spatial layout of these colors.
However, two drawbacks of SMOG are still obvious, firstly, in the initialization of SMOG, some background pixels are
inevitably introduced and clustered as an object mode for tracking, this often degenerates the tracking performance.
Secondly, the weight of each Gaussian mode is restricted by the probability of the pixels belong to it, so a low
probability Gaussian mode always contribute a little in similarity measure even it has a high discrimination for
discriminating the object. A revised SMOG model is proposed to efficiently cope with these two problems by sufficiently
considering the object local background. Experiment results on synthetic and real image sequences verified the validity
of the revised model.