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
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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