Explicit state duration HMM for abnormality detection in sequences of human activity
Much of the current work in human behaviour modelling concentrates on activity recognition, recognising actions and events through pose, movement, and gesture analysis. Our work focuses on learning and detecting abnormality in higher level behavioural patterns. The hidden Markov model (HMM) is one a...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Springer-Verlag
2004
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| Online Access: | http://hdl.handle.net/20.500.11937/8946 |
| _version_ | 1848745807850766336 |
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| author | Luhr, Sebastian Venkatesh, Svetha West, Geoffrey Bui, Hung H. |
| author2 | Chengqi Zhang |
| author_facet | Chengqi Zhang Luhr, Sebastian Venkatesh, Svetha West, Geoffrey Bui, Hung H. |
| author_sort | Luhr, Sebastian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Much of the current work in human behaviour modelling concentrates on activity recognition, recognising actions and events through pose, movement, and gesture analysis. Our work focuses on learning and detecting abnormality in higher level behavioural patterns. The hidden Markov model (HMM) is one approach for learning such behaviours given a vision tracker recording observations about a persons activity. Duration of human activity is an important consideration if we are to accurately model a persons behavioural patterns. We show how the implicit state duration in the HMM can create a situation in which highly abnormal deviation as either less than or more than the usually observed activity duration can fail to be detected and how the explicit state duration HMM (ESD-HMM) helps alleviate the problem. |
| first_indexed | 2025-11-14T06:23:14Z |
| format | Conference Paper |
| id | curtin-20.500.11937-8946 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:23:14Z |
| publishDate | 2004 |
| publisher | Springer-Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-89462022-10-06T07:11:27Z Explicit state duration HMM for abnormality detection in sequences of human activity Luhr, Sebastian Venkatesh, Svetha West, Geoffrey Bui, Hung H. Chengqi Zhang Hans W Guesgen Wai K Yeap Much of the current work in human behaviour modelling concentrates on activity recognition, recognising actions and events through pose, movement, and gesture analysis. Our work focuses on learning and detecting abnormality in higher level behavioural patterns. The hidden Markov model (HMM) is one approach for learning such behaviours given a vision tracker recording observations about a persons activity. Duration of human activity is an important consideration if we are to accurately model a persons behavioural patterns. We show how the implicit state duration in the HMM can create a situation in which highly abnormal deviation as either less than or more than the usually observed activity duration can fail to be detected and how the explicit state duration HMM (ESD-HMM) helps alleviate the problem. 2004 Conference Paper http://hdl.handle.net/20.500.11937/8946 10.1007/978-3-540-28633-2_125 Springer-Verlag restricted |
| spellingShingle | Luhr, Sebastian Venkatesh, Svetha West, Geoffrey Bui, Hung H. Explicit state duration HMM for abnormality detection in sequences of human activity |
| title | Explicit state duration HMM for abnormality detection in sequences of human activity |
| title_full | Explicit state duration HMM for abnormality detection in sequences of human activity |
| title_fullStr | Explicit state duration HMM for abnormality detection in sequences of human activity |
| title_full_unstemmed | Explicit state duration HMM for abnormality detection in sequences of human activity |
| title_short | Explicit state duration HMM for abnormality detection in sequences of human activity |
| title_sort | explicit state duration hmm for abnormality detection in sequences of human activity |
| url | http://hdl.handle.net/20.500.11937/8946 |