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Belief Theory-Based Classifiers Comparison for Static Human Body Postures Recognition in Video

Authors: V. Girondel, L. Bonnaud, A. Caplier, M. Rombaut


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.

Keywords: Data fusion, human motion analysis, real-time processing, Belief theory, classifiers comparison, static posture recognition

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