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. Attached Files Name Description MIMEType Size Downloads duong-humanbehavior-2006.pdf Published version application/pdf 316.80KB 161 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.org/0000-0002-9977-8247 Bui, Hung H. 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 Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 20 Apr 2012, 11:34:46 EST Fri, 27 Apr 2012, 13:36:43 EST Fri, 27 Apr 2012, 13:38:34 EST Thu, 03 May 2012, 11:33:42 EST Tue, 06 Sep 2016, 18:45:18 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 25 times in Scopus Search Google Scholar Access Statistics: 260 Abstract Views, 161 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:34:46 EST

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