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dc.contributor.authorDuong, Thi
dc.contributor.authorBui, Hung H.
dc.contributor.authorPhung, Dinh
dc.contributor.authorVenkatesh, Svetha
dc.contributor.editorSchnid, C. and Soatto, S. and Tomasi, C.
dc.date.accessioned2017-01-30T11:18:19Z
dc.date.available2017-01-30T11:18:19Z
dc.date.created2009-03-05T00:55:23Z
dc.date.issued2005
dc.identifier.citationDuong, Thi and Bui, Hung H. and Phung, Dinh and Venkatesh, Svetha. 2005. Activity recognition and abnormality detection with the switching hidden semi-Markov model, in Schnid, C. and Soatto, S. and Tomasi, C. (ed), Conference on Computer Vision and Pattern Recognition (CVPR 2005), Jun 20 2005, Vol. 1: pp. 838-845. San Diego, USA: IEEE Computer Society Press.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/10349
dc.identifier.doi10.1109/CVPR.2005.61
dc.description.abstract

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which isan 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 usingmultinomial 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-HSMMperforms 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.

dc.publisherIEEE Computer Society Press
dc.titleActivity recognition and abnormality detection with the switching hidden semi-Markov model
dc.typeConference Paper
dcterms.source.volume1
dcterms.source.startPage838
dcterms.source.endPage845
dcterms.source.issn10636919
dcterms.source.titleProceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
dcterms.source.seriesProceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
dcterms.source.isbn0769523722
dcterms.source.conferenceConference on Computer Vision and Pattern Recognition (CVPR 2005)
dcterms.source.conference-start-date20 Jun 2005
dcterms.source.conferencelocationSan Diego, USA
dcterms.source.placeLos Alamitos, USA
curtin.note

Copyright © 2005 IEEE. 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.

curtin.accessStatusOpen access
curtin.facultySchool of Electrical Engineering and Computing
curtin.facultyDepartment of Computing
curtin.facultyFaculty of Science and Engineering


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