Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
dc.contributor.author | Nguyen, Nam | |
dc.contributor.author | Phung, Dinh | |
dc.contributor.author | Venkatesh, Svetha | |
dc.contributor.author | Bui, Hung H. | |
dc.contributor.editor | Schnid, C. | |
dc.contributor.editor | Soatto, S. | |
dc.contributor.editor | Tomasi, C. | |
dc.date.accessioned | 2017-01-30T11:49:06Z | |
dc.date.available | 2017-01-30T11:49:06Z | |
dc.date.created | 2009-03-05T00:55:23Z | |
dc.date.issued | 2005 | |
dc.identifier.citation | Nguyen, 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.uri | http://hdl.handle.net/20.500.11937/15305 | |
dc.identifier.doi | 10.1109/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.publisher | IEEE Computer Society Press | |
dc.title | Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model | |
dc.type | Conference Paper | |
dcterms.source.volume | 2 | |
dcterms.source.startPage | 955 | |
dcterms.source.endPage | 960 | |
dcterms.source.issn | 10636919 | |
dcterms.source.title | Proceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition | |
dcterms.source.series | Proceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition | |
dcterms.source.isbn | 0769523722 | |
dcterms.source.conference | Conference on Computer Vision and Pattern Recognition (CVPR 2005) | |
dcterms.source.conference-start-date | 20 Jun 2005 | |
dcterms.source.conferencelocation | San Diego, USA | |
dcterms.source.place | Los 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.accessStatus | Open access | |
curtin.faculty | School of Electrical Engineering and Computing | |
curtin.faculty | Department of Computing | |
curtin.faculty | Faculty of Science and Engineering |