Department of Computer Science
Informatik 9
Boltzmannstrasse 3
85748 Garching
Germany
Tel: +49-89-289-17759
Fax: +49-89-289-17757
Office:
Mail: michael.beetz@in.tum.de
CCRL II:
Tel: +49-89-289-26902
Office: 3002
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Michael Beetz is a professor for Computer Science at the Department of Informatics of the Technische Universität Muenchen and heads the Intelligent Autonomous Systems group. From 2006 to 2011, he was vice coordinator of the German national cluster of excellence CoTeSys (Cognition for Technical Systems) where he is also co-coordinator of the research area “Knowledge and Learning”.
Michael Beetz received his diploma degree in Computer Science with distinction from the University of Kaiserslautern. He received his MSc, MPhil, and PhD degrees from Yale University in 1993, 1994, and 1996 and his Venia Legendi from the University of Bonn in 2000. Michael Beetz was a member of the steering committee of the European network of excellence in AI planning (PLANET) and coordinating the research area ``robot planning''. He is associate editor of the AI Journal. His research interests include plan-based control of robotic agents, knowledge processing and representation for robots, integrated robot learning, and cognitive perception.
@InProceedings{Rusu08ROMAN,
author = {Radu Bogdan Rusu and Jan Bandouch and Zoltan Csaba Marton and Nico Blodow and Michael Beetz},
title = {{Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences}},
booktitle = {IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany},
year = {2008},
bib2html_pubtype = {Conference Paper},
bib2html_rescat = {Perception},
bib2html_groups = {Memoman, EnvMod},
bib2html_funding = {CoTeSys},
bib2html_domain = {Assistive Household},
abstract = {
In this paper we present our work on human action recognition in intelligent
environments. We classify actions by looking at a time-sequence of
silhouettes extracted from various camera images. By treating time as the
third spatial dimension we generate so-called space-time shapes that contain
rich information about the actions. We propose a novel approach for
recognizing actions, by representing the shapes as 3D point clouds and
estimating feature histograms for them. Preliminary results show that our
method robustly derives different classes of actions, even in the presence
of large variability in the data, coming from different persons at different
time intervals.
}
}