Source:http://linkedlifedata.com/resource/pubmed/id/18390385
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rdf:type | |
lifeskim:mentions | |
pubmed:issue |
5
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pubmed:dateCreated |
2008-4-8
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pubmed:abstractText |
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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pubmed:language |
eng
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pubmed:journal | |
pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:month |
May
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pubmed:issn |
1057-7149
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pubmed:author | |
pubmed:issnType |
Print
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pubmed:volume |
17
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
811-22
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pubmed:meshHeading |
pubmed-meshheading:18390385-Algorithms,
pubmed-meshheading:18390385-Artificial Intelligence,
pubmed-meshheading:18390385-Computer Simulation,
pubmed-meshheading:18390385-Image Enhancement,
pubmed-meshheading:18390385-Image Interpretation, Computer-Assisted,
pubmed-meshheading:18390385-Information Storage and Retrieval,
pubmed-meshheading:18390385-Markov Chains,
pubmed-meshheading:18390385-Models, Statistical,
pubmed-meshheading:18390385-Pattern Recognition, Automated,
pubmed-meshheading:18390385-Reproducibility of Results,
pubmed-meshheading:18390385-Sensitivity and Specificity,
pubmed-meshheading:18390385-Subtraction Technique,
pubmed-meshheading:18390385-Video Recording
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pubmed:year |
2008
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pubmed:articleTitle |
Infinite hidden Markov models for unusual-event detection in video.
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pubmed:affiliation |
Electrical and Computer Engineering Department, Duke University, Durham, NC 27708-0291, USA. ip6@ee.duke.edu
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pubmed:publicationType |
Journal Article,
Evaluation Studies
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