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A new approach to tracking using active contour model is presented. Suppose that the class of objects to be tracked is characterized by a probability distribution, we tackle the active contour tracking problem by learning a suitable distance measure between distributions. A cross bin criterion for comparing distributions in quadratic form is adopted in this paper for active contour tracking, in which...
In this paper we present a novel method of using genetic algorithm (GA) to learn a graphical model which is used for human motion characterization. The modeling of human movements will involve a high dimensional joint probability density function. With this graphical model, the joint probability distribution can be decomposed into a number of low dimensional distributions which are represented as...
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