GHT based implementation of the expectation maximization for mixtures of multi-Gaussians and its applications to video tracking

In this work, the problem of the estimation of parameters in case of mixtures of models composed of the sum of multiple Gaussians is considered. It will be shown how this estimation can be performed efficiently by using the Generalized Hough Transform (GHT). The theoretical results will be applied to a corner-based object tracking application considering, in particular, the case of two or more objects that come into proximity and occlude each other. Quantitative results show the performances of the derived algorithm both on synthetically generated data and real tracking sequences.