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 people 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. Keywords—belief theory, face detection, human body analysis, human posture recognition, real-time processing, skin detection.