Human Posture Tracking and Classification through Stereo Vision and 3D Model Matching. (7th April 2008)

• Record Type: Journal Article
• Authors: Pellegrini, Stefano; Iocchi, Luca
• Other Names: Pitas Ioannis Academic Editor.
• Is Part Of: EURASIP journal on image and video processing. Volume 2008(2008)
• Publication Date: 2008-04-07
• Subjects: Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; Traitement d'images; Vidéo numérique; Digital video; Image processing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 621.367
• DOI: 10.1155/2008/476151
• Languages: English
• ISSNs: 1687-5176

Abstract

Abstract : The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. This problem has been studied in recent years in the computer vision community, but the proposed solutions still suffer from some limitations due to the difficulty of dealing with complex scenes (e.g., occlusions, different view points, etc.). In this article, we present a system for posture tracking and classification based on a stereo vision sensor. The system provides both a robust way to segment and track people in the scene and 3D information about tracked people. The proposed method is based on matching 3D data with a 3D human body model. Relevant points in the model are then tracked over time with temporal filters and a classification method based on hidden Markov models is used to recognize principal postures. Experimental results show the effectiveness of the system in determining human postures with different orientations of the people with respect to the stereo sensor, in presence of partial occlusions and under different environmental conditions.