Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model Nguyen, Nam T., Phung, Dinh Q., Venkatesh, Svetha and Bui, Hung 2005, Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model, in CVPR 2005 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Los Alamitos, Calif., pp. 955-960, doi: 10.1109/CVPR.2005.203. Attached Files Name Description MIMEType Size Downloads venkatesh-learningand-2005.pdf Published version application/pdf 192.97KB 1165 Title Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model Author(s) Nguyen, Nam T. Phung, Dinh Q. orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Bui, Hung Conference name 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 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Editor(s) Schmid, C. Soatto, S. Tomasi, C. Publication date 2005 Conference series Computer Vision and Pattern Recognition. Conference Start page 955 End page 960 Total pages 6 Publisher IEEE Place of publication Los Alamitos, Calif. Keyword(s) artificial intelligence distributed computing hidden Markov models humans inference algorithms intelligent sensors learning particle filters robustness stochastic processes Summary Directly modeling the inherent hierarchy and shared structures of human behaviors, 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 modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM. 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 DOI 10.1109/CVPR.2005.203 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:30044617 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, 11:35:39 EST Fri, 27 Apr 2012, 14:56:27 EST Fri, 27 Apr 2012, 14:58:10 EST Thu, 03 May 2012, 12:07:33 EST Sat, 21 Oct 2017, 02:03:42 EST Tue, 05 Dec 2017, 12:31:34 EST Filtered Full Citation counts:   Cited 84 times in TR Web of Science Cited 218 times in Scopus Search Google Scholar Access Statistics: 360 Abstract Views, 1165 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:35:39 EST

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