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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.

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Title Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
Author(s) Nguyen, Nam T.
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
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044617

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.