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Explicit state duration HMM for abnormality detection in sequences of human activity
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posted on 2004-01-01, 00:00 authored by S Luhr, Svetha VenkateshSvetha Venkatesh, G West, H BuiThe 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.
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Title of book
PRICAI 2004 : trends in artificial intelligence : 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, August 9-13, 2004 : proceedingsSeries
Lecture notes in artificial intelligence ; 3157Chapter number
125Pagination
983 - 984Publisher
Springer-VerlagPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743ISBN-13
9783540228172ISBN-10
3540228179Language
engPublication classification
B1.1 Book chapterCopyright notice
2004, Springer-Verlag Berlin HeidelbergExtent
142Editor/Contributor(s)
C Zhang, H Guesgen, W YeapUsage metrics
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