Deakin University
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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.



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Publication classification

E1.1 Full written paper - refereed

Copyright notice

2005, IEEE


C Schmid, S Soatto, C Tomasi

Title of proceedings

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