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Localized anomaly detection via hierarchical integrated activity discovery

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dc.contributor.authorChockalingam, Thiyagarajan, author
dc.contributor.authorRajopadhye, Sanjay, advisor
dc.contributor.authorAnderson, Chuck, advisor
dc.contributor.authorPasricha, Sudeep, committee member
dc.contributor.authorBohm, Wim, committee member
dc.date.accessioned2007-01-03T06:38:52Z
dc.date.available2007-01-03T06:38:52Z
dc.date.issued2014
dc.description.abstractWith the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this thesis, we consider recent methods based on temporal probabilistic models and improve them in multiple ways. Our contributions are the following: (i) we integrate the low level processing and the temporal activity modeling, showing how this feedback improves the overall quality of the captured information, (ii) we show how the same approach can be taken to do hierarchical multi-camera processing, (iii) we use spatial analysis of the anomalies both to perform local anomaly detection and to frame automatically the detected anomalies. We illustrate the approach on both traffic data and videos coming from a metro station. We also investigate the application of topic models in Brain Computing Interfaces for Mental Task classification. We observe a classification accuracy of up to 68% for four Mental Tasks on individual subjects.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierChockalingam_colostate_0053N_12168.pdf
dc.identifier.urihttp://hdl.handle.net/10217/82491
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectbrain computing interfaces
dc.subjectDirichlet prior
dc.subjectPLSA
dc.subjectPLSM
dc.subjectsurveillance
dc.subjecttopic modelling
dc.titleLocalized anomaly detection via hierarchical integrated activity discovery
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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