Open Access

Human Posture Tracking and Classification through Stereo Vision and 3D Model Matching

EURASIP Journal on Image and Video Processing20072008:476151

DOI: 10.1155/2008/476151

Received: 15 February 2007

Accepted: 22 November 2007

Published: 16 December 2007


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.

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Authors’ Affiliations

Dipartimento di Informatica e Sistemistica, Università degli Studi di Roma "Sapienza"


© S. Pellegrini and L. Iocchi. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.