A belief theory-based static posture recognition systems for real-time video surveillance applications

This paper presents a system that can automatically recognize four different static human body postures for video surveillance applications. The considered postures are standing, sitting, squatting, and lying. The data come from the persons 2D segmentation and from their face localization. It consists in distance measurements relative to a reference posture (standing, arms stretched horizontally). The recognition is based on data fusion using the belief theory, because this theory allows the modelling of imprecision and uncertainty. The efficiency and the limits of the recognition system are highlighted thanks to the processing of several thousands of frames. A considered application is the monitoring of elder people in hospitals or at home. This system allows real-time processing.