venkatesh-learningand-2005.pdf (192.98 kB)
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
conference contribution
posted on 2005-01-01, 00:00 authored by N Nguyen, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh, H BuiDirectly 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.
History
Event
Conference on Computer Vision and Pattern Recognition (2005 : San Diego, Calif.)Pagination
955 - 960Publisher
IEEELocation
San Diego, Calif.Place of publication
Los Alamitos, Calif.Publisher DOI
Start date
2005-06-20End date
2005-06-25ISSN
1063-6919ISBN-13
9780769523729ISBN-10
0769523722Language
engNotes
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.Publication classification
E1.1 Full written paper - refereedCopyright notice
2005, IEEEEditor/Contributor(s)
C Schmid, S Soatto, C TomasiTitle of proceedings
CVPR 2005 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern RecognitionUsage metrics
Categories
No categories selectedKeywords
artificial intelligencedistributed computinghidden Markov modelshumansinference algorithmsintelligent sensorslearningparticle filtersrobustnessstochastic processesScience & TechnologyTechnologyComputer Science, Artificial IntelligenceImaging Science & Photographic TechnologyComputer ScienceRECOGNITIONBEHAVIOR
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC