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Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

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conference contribution
posted on 2005-01-01, 00:00 authored by N Nguyen, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh, H Bui
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.

History

Pagination

955 - 960

Location

San Diego, Calif.

Open access

  • Yes

Start date

2005-06-20

End date

2005-06-25

ISSN

1063-6919

ISBN-13

9780769523729

ISBN-10

0769523722

Language

eng

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.

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2005, IEEE

Editor/Contributor(s)

C Schmid, S Soatto, C Tomasi

Title of proceedings

CVPR 2005 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition