Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model
Duong, Thi V., Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2006, Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model, in ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 202-207.
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.
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ISBN
0769525210 9780769525211
ISSN
1051-4651
Language
eng
Field of Research
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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