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Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model

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conference contribution
posted on 2006-01-01, 00:00 authored by Thi Duong, Quoc-Dinh Phung, H Bui, Svetha VenkateshSvetha Venkatesh
The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the state-of-the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic Bayesian network (DBN). We investigate both discrete and continuous distributions from the exponential family (Poisson and Inverse Gaussian respectively) for the problem of learning and recognizing ADLs. A full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new Coxian are also presented. Our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.

History

Pagination

202 - 207

Location

Hong Kong, China

Open access

  • Yes

Start date

2006-08-20

End date

2006-08-24

ISSN

1051-4651

ISBN-13

9780769525211

ISBN-10

0769525210

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

2006, IEEE

Title of proceedings

ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition

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