Learning graphical model for human motion characterization using genetic optimization

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 tree models and triangulated models. To automatically search for such a model from a database of cases is a NP-hard problem. We use GA to solve this problem, which can optimize both the ordering structure and the conditional independence relationship of the graphical model. The searched graphical models are used to classify different types of human motions. The experimental results demonstrate that, compared with a previous greedy search algorithm, the GA is more effective for optimization of the graphical model.