A human body analysis system

This paper describes a system for human body analysis ( segmentation, tracking, face/hands localisation, posture recognition) from a single view that is fast and completely automatic. The system first extracts low-level data and uses part of the data for high-level interpretation. It can detect and track several persons even if they merge or are completely occluded by another person from the camera's point of view. For the high-level interpretation step, static posture recognition is performed using a belief theory-based classifier. The belief theory is considered here as a new approach for performing posture recognition and classification using imprecise and/or conflicting data. Four different static postures are considered: standing, sitting, squatting, and lying. The aim of this paper is to give a global view and an evaluation of the performances of the entire system and to describe in detail each of its processing steps, whereas our previous publications focused on a single part of the system. The efficiency and the limits of the system have been highlighted on a database of more than fifty video sequences where a dozen different individuals appear. This system allows real-time processing and aims at monitoring elderly people in video surveillance applications or at the mixing of real and virtual worlds in ambient intelligence systems.