A scenario-based query processing framework for video surveillance

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buir.co-supervisorUlusoy, Özgür
buir.supervisorGüdükbay, Uğur
dc.contributor.authorŞaykol, Ediz
dc.date.accessioned2016-01-08T18:11:31Z
dc.date.available2016-01-08T18:11:31Z
dc.date.issued2009
dc.descriptionCataloged from PDF version of article.
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.en_US
dc.descriptionThesis (Ph. D.) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references (leaves 78-84).en_US
dc.description.abstractVideo surveillance has become one of the most interesting and challenging application areas in video processing domain. Automated access to the semantic content of surveillance videos to detect anomalies is among the basic tasks; however due to the high variability of the visual features and large size of the video input, it still remains a challenging issue. A considerable amount of research dealing with automated access to video surveillance has appeared in the literature; however, significant semantic gaps in event models and content-based access still remain. In this thesis, we propose a scenario-based query processing framework for video surveillance archives, especially for indoor environments. A scenario is specified as a sequence of event predicates that can be enriched with object-based low—level features and directional predicates. We also propose a keyframe labeling technique, which assigns labels to keyframes extracted based on keyframe detection algorithm, hence transforms the input video to an event sequence based representation. The keyframe detection scheme relies on an inverted tracking scheme, which is a view-based representation of the actual content by an inverted index. We also devise mechanisms based on finite state automata using this event sequence representation to detect a typical set of anomalous events in the scene, which are also used for meta-data extraction. Our query processing framework also supports inverse querying and view-based querying, for after-the-fact activity analysis, since the inverted tracking scheme effectively tracks the moving objects and enables view-based addressing of the scene. We propose a specific surveillance query language to express the supported query types in a scenario-based manner. We also present a visual query specification interface devised to enhance the query-specification process. It has been shown through performance experiments that the keyframe labeling algorithm significantly reduces the storage requirements and yields a reasonable anomaly detection performance. We have also conducted performance experiments to show that our query processing technique has a high expressive power and satisfactory retrieval accuracy in video surveillance.
dc.description.provenanceMade available in DSpace on 2016-01-08T18:11:31Z (GMT). No. of bitstreams: 1 0003929.pdf: 1004838 bytes, checksum: 49821fd37bdf4c8aae06f0a3077e406f (MD5)en
dc.description.statementofresponsibilityby Ediz Şaykolen_US
dc.format.extentxvii, 94 leaves : illustrations ; 30 cm.en_US
dc.identifier.itemidB118456
dc.identifier.urihttp://hdl.handle.net/11693/14958
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectVideo surveillance
dc.subjectScenario-based querying
dc.subjectKeyframe labeling
dc.subjectInverse querying
dc.subjectView-based querying
dc.subjectAnomaly detection
dc.subjectAfter-the-fact analysis
dc.titleA scenario-based query processing framework for video surveillanceen_US
dc.title.alternativeGözetim videoları için senaryo tabanlı sorgulama çatısı
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelDoctoral
thesis.degree.namePh.D. (Doctor of Philosophy)

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