Abstract.
In this paper, we adaptively model the appearance of objects based  on Mixture of Gaussians in a joint spatial-color space (the approach is called  SMOG). We propose a new SMOG-based similarity measure. SMOG captures  richer information than the general color histogram because it incorporates spa- tial layout in addition to color. This appearance model and the similarity meas- ure are used in a framework of Bayesian probability for tracking natural objects.  In the second part of the paper, we propose an Integral Gaussian Mixture (IGM)  technique, as a fast way to extract the parameters of SMOG for target candidate.  With IGM, the parameters of SMOG can be computed efficiently by using only  simple arithmetic operations (addition, subtraction, division) and thus the com- putation is reduced to linear complexity. Experiments show that our method can  successfully track objects despite changes in foreground appearance, clutter,  occlusion, etc.; and that it outperforms several color-histogram based methods.