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Explicit state duration HMM for abnormality detection in sequences of human activity

Luhr, Sebastian, Venkatesh, Svetha, West, Geoff A. W. and Bui, Hung H. 2004, Explicit state duration HMM for abnormality detection in sequences of human activity. In Zhang, Chengqi, Guesgen, Hans W. and Yeap, Wai K. (ed), PRICAI 2004 : trends in artificial intelligence : 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, August 9-13, 2004 : proceedings, Springer-Verlag, Berlin, Germany, pp.983-984, doi: 10.1007/978-3-540-28633-2_125.

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Title Explicit state duration HMM for abnormality detection in sequences of human activity
Author(s) Luhr, Sebastian
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
West, Geoff A. W.
Bui, Hung H.
Title of book PRICAI 2004 : trends in artificial intelligence : 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, August 9-13, 2004 : proceedings
Editor(s) Zhang, Chengqi
Guesgen, Hans W.
Yeap, Wai K.
Publication date 2004
Series Lecture notes in artificial intelligence ; 3157
Chapter number 125
Total chapters 142
Start page 983
End page 984
Total pages 2
Publisher Springer-Verlag
Place of Publication Berlin, Germany
Keyword(s) abnormal activity
behaviour patterns
models
Summary The importance of explicit duration modelling for classification of sequences of human activity and the reliable and timely detection of duration abnormality was highlighted. The normal classes of behavior were designed to highlight the importance of modelling duration given the limitations of the tracking system. It was found that HMM was the weakest model for classification of the unseen normal sequences with 81% accuracy. Long term abnormality was investigated by artificially varying the duration of primary activity in a randomly selected test sequence. The incorporation of duration in models of human behavior is an important consideration for systems seeking to provide cognitive support and to detect deviation in the behavorial patterns.
ISBN 9783540228172
3540228179
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-540-28633-2_125
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1.1 Book chapter
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044660

Document type: Book Chapter
Collection: School of Information Technology
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