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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.

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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 iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for 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
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044616

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.