Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model

Authors:
Thi V. Duong;Dinh Q. Phung;Hung H. Bui;Svetha Venkatesh
Affiliations:
Curtin University of Technology, Perth, WA;Curtin University of Technology, Perth, WA;Curtin University of Technology, Perth, WA;Curtin University of Technology, Perth, WA
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 6

Recognition of User Intentions for Interface Agents with Variable Order Markov Models

UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Goal recognition with variable-order Markov models

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Building respectful interface agents

International Journal of Human-Computer Studies
Temporal maximum margin Markov network

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Modeling sequences of user actions for statistical goal recognition

User Modeling and User-Adapted Interaction
Towards a goal recognition model for the organizational memory

ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 stateof- 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.