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Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model

Duong, Thi V., Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2006, Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model, in ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 202-207.

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Title Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model
Author(s) Duong, Thi V.
Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Pattern Recognition (18th : 2006 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 20-24 Aug. 2006
Title of proceedings ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2006
Conference series International Conference on Pattern Recognition
Start page 202
End page 207
Total pages 6
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) generic exponential family duration modeling
hidden semi markov models
human behavior recognition
model state durations
Summary The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the state-of-the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic Bayesian network (DBN). We investigate both discrete and continuous distributions from the exponential family (Poisson and Inverse Gaussian respectively) for the problem of learning and recognizing ADLs. A full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new Coxian are also presented. Our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0769525210
9780769525211
ISSN 1051-4651
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2006, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044603

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.