A belief theory-based static posture recognition system for real-time videosurveillance applications

Vincent Girondel (1) , Alice Caplier (1) , Laurent Bonnaud (1)

1 LIS - Laboratoire des images et des signaux

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Vincent Girondel, Alice Caplier, Laurent Bonnaud. A belief theory-based static posture recognition system for real-time videosurveillance applications. IEEE International Conference on Advanced Video and Signal based Surveillance - AVSS, Sep 2005, Como, Italy. pp.10-15. ⟨hal-00156543⟩