Authors:
Stefano Pellegrini
and
Luca Iocchi
Affiliation:
Univesità di Roma “La Sapienza”, Italy
Keyword(s):
Human Posture Classification, Human Activity Recognition, 3D Modeling, Stereo Vision.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Model-Based Object Tracking in Image Sequences
;
Motion, Tracking and Stereo Vision
;
Tracking of People and Surveillance
Abstract:
The ability of detecting human postures is very relevant for applications related to the analysis of human behaviors. Techniques for posture detection and classification can be thus very important in several fields, like ambient intelligence, surveillance, elderly care, etc. This problem has been studied in recent years in the Computer Vision community, but proposed solutions still suffer from some limitations that are due to the difficulty of dealing with complex scenes (e.g., occlusions, different view points, etc.). In this paper we present a system for posture tracking and classification that uses a stereo vision sensor, which provides both for a robust way to segment and track people in the scene and 3D information about tracked people. The presented method uses a 3D model of human body, performs model matching through a variant of the ICP algorithm and then uses a Hidden Markov Model to model posture transitions. Experimental results show the effectiveness of the system in dete
rmining human postures in presence of partial occlusions and from different view points.
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