Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Coputer Society Conference Publishing Services
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/46984 |
| _version_ | 1848757710268399616 |
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| author | Duong, Thi Phung, Dinh Bui, H.H. Venkatesh, Svetha |
| author2 | Y.Y. Tang |
| author_facet | Y.Y. Tang Duong, Thi Phung, Dinh Bui, H.H. Venkatesh, Svetha |
| author_sort | Duong, Thi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:32:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-46984 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:32:25Z |
| publishDate | 2006 |
| publisher | IEEE Coputer Society Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-469842023-02-27T07:34:29Z Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model Duong, Thi Phung, Dinh Bui, H.H. Venkatesh, Svetha Y.Y. Tang S.P.Wang G. Lorette D.S. Young H. Yang 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. 2006 Conference Paper http://hdl.handle.net/20.500.11937/46984 10.1109/ICPR.2006.635 IEEE Coputer Society Conference Publishing Services restricted |
| spellingShingle | Duong, Thi Phung, Dinh Bui, H.H. Venkatesh, Svetha Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model |
| title | Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model |
| title_full | Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model |
| title_fullStr | Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model |
| title_full_unstemmed | Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model |
| title_short | Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model |
| title_sort | human behavior recognition with generic exponential family duration modeling in the hidden semi-markov model |
| url | http://hdl.handle.net/20.500.11937/46984 |