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dc.contributor.authorNguyen, Nam
dc.contributor.authorPhung, Dinh
dc.contributor.authorVenkatesh, Svetha
dc.contributor.authorBui, Hung H.
dc.contributor.editorSchnid, C. and Soatto, S. and Tomasi, C.
dc.date.accessioned2017-01-30T11:49:06Z
dc.date.available2017-01-30T11:49:06Z
dc.date.created2009-03-05T00:55:23Z
dc.date.issued2005
dc.identifier.citationNguyen, Nam and Phung, Dinh and Venkatesh, Svetha and Bui, Hung. 2005. Learning and detecting activities from movement trajectories using the hierarchical hidden 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. 2: pp. 955-960. San Diego, USA: IEEE Computer Society Press.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/15305
dc.identifier.doi10.1109/CVPR.2005.203
dc.description.abstract

Directly modelling the inherent hierarchy and shared structures of human behaviours, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model?s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modelling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

dc.publisherIEEE Computer Society Press
dc.titleLearning and detecting activities from movement trajectories using the hierarchical hidden Markov model
dc.typeConference Paper
dcterms.source.volume2
dcterms.source.startPage955
dcterms.source.endPage960
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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