Activity recognition and abnormality detection with the switching hidden semi-Markov model Duong, Thi V., Bui, Hung H., Phung, Dinh Q. and Venkatesh, Svetha 2005, Activity recognition and abnormality detection with the switching hidden semi-Markov model, in CVPR 2005 : Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Washington, D. C., pp. 838-845. Attached Files Name Description MIMEType Size Downloads duong-activityrecognition-2005.pdf Published version application/pdf 203.18KB 1737 Title Activity recognition and abnormality detection with the switching hidden semi-Markov model Author(s) Duong, Thi V. Bui, Hung H. Phung, Dinh Q. orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005 : San Diego, Calif.) Conference location San Diego, Calif. Conference dates 20-25 Jun. 2005 Title of proceedings CVPR 2005 : Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Editor(s) [Unknown] Publication date 2005 Conference series IEEE Computer Society Conference on Computer Vision and Pattern Recognition Start page 838 End page 845 Total pages 8 Publisher IEEE Place of publication Washington, D. C. Keyword(s) abnormality detection tasks activities of daily living (ADL) coxian duration model switching hidden semi-Markov model Summary This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model. 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 0769523722 9780769523729 ISSN 1063-6919 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 ©2005, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30044616 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, 11:35:36 EST Fri, 27 Apr 2012, 15:16:13 EST Fri, 27 Apr 2012, 15:18:03 EST Thu, 03 May 2012, 12:05:03 EST Tue, 06 Sep 2016, 18:46:05 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 319 times in Scopus Search Google Scholar Access Statistics: 345 Abstract Views, 1737 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:35:36 EST

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