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 determining human postures in presence of partial occlusions and from different view points.
Human posture tracking and classification through stereo vision / Pellegrini, Stefano; Iocchi, Luca. - 2:(2006), pp. 261-269. (Intervento presentato al convegno VISAPP 2006 - 1st International Conference on Computer Vision Theory and Applications tenutosi a Setubal; Portugal nel 25 February 2006 through 28 February 2006).
Human posture tracking and classification through stereo vision
PELLEGRINI, STEFANO;IOCCHI, Luca
2006
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 determining human postures in presence of partial occlusions and from different view points.File | Dimensione | Formato | |
---|---|---|---|
VE_2006_11573-367320.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
2.88 MB
Formato
Adobe PDF
|
2.88 MB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.