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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.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.

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.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.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

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

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