A Human Body Analysis System
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A Human Body Analysis System Vincent Girondel, Laurent Bonnaud, and Alice Caplier Laboratoire des Images et des Signaux (LIS), INPG, 38031 Grenoble, France Received 20 July 2005; Revised 10 January 2006; Accepted 21 January 2006 Recommended for Publication by Irene Y. H. Gu 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 theorybased 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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Human motion analysis is an important area of research in computer vision devoted to detecting, tracking, and understanding people’s physical behaviour. This strong interest is driven by a wide spectrum of applications in various areas such as smart video surveillance [1], interactive virtual reality systems [2, 3], advanced and perceptual human-computer interfaces (HCI) [4], model-based coding [5], content-based video storage and retrieval [6], sports performances analysis and enhancement [7], clinical studies [8], smart rooms and ambient intelligence systems [9, 10], and so forth. The “looking at people” research field has recently received a lot of attention [11–16]. Here, the considered applications are video surveillance and smart rooms with advanced HCIs. Video surveillance covers applications where people are being tracked and monitored for particular actions. The demand for smart video surveillance systems comes from the existence of security-sensitive areas such as banks, department stores, parking lots, and so forth. Surveillance cameras video streams are often stored in video archives or recorded on tapes. Most of the time, these video streams are only used “after the fact” mainly as an identification tool. The fact that the camera is an active sensor and a real-time processing media is therefore sometimes unused. The n
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