%0 Journal Article
	%A V. Girondel and  L. Bonnaud and  A. Caplier and  M. Rombaut
	%D 2007
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 4, 2007
	%T Belief Theory-Based Classifiers Comparison for Static Human Body Postures Recognition in Video
	%U https://publications.waset.org/pdf/3907
	%V 4
	%X This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.

	%P 1078 - 1081