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231223s2008 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2008.919359
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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|a Pruteanu-Malinici, Iulian
|e verfasserin
|4 aut
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|a Infinite hidden Markov models for unusual-event detection in video
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|c 2008
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 10.06.2008
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a We address the problem of unusual-event detection in a video sequence. Invariant subspace analysis (ISA) is used to extract features from the video, and the time-evolving properties of these features are modeled via an infinite hidden Markov model (iHMM), which is trained using "normal"/"typical" video. The iHMM retains a full posterior density function on all model parameters, including the number of underlying HMM states. Anomalies (unusual events) are detected subsequently if a low likelihood is observed when associated sequential features are submitted to the trained iHMM. A hierarchical Dirichlet process framework is employed in the formulation of the iHMM. The evaluation of posterior distributions for the iHMM is achieved in two ways: via Markov chain Monte Carlo and using a variational Bayes formulation. Comparisons are made to modeling based on conventional maximum-likelihood-based HMMs, as well as to Dirichlet-process-based Gaussian-mixture models
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|a Evaluation Study
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|a Journal Article
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|a Carin, Lawrence
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 17(2008), 5 vom: 01. Mai, Seite 811-22
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
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|g volume:17
|g year:2008
|g number:5
|g day:01
|g month:05
|g pages:811-22
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|u http://dx.doi.org/10.1109/TIP.2008.919359
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