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dc.contributor.advisorDuygulu, Pınaren_US
dc.contributor.authorİkizler, Nazlıen_US
dc.date.accessioned2016-01-08T18:06:15Z
dc.date.available2016-01-08T18:06:15Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/11693/14729
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.en_US
dc.descriptionThesis (Ph.D.) -- Bilkent University, 2008.en_US
dc.descriptionIncludes bibliographical references leaves 111-121.en_US
dc.description.abstractWithin the ever-growing video archives is a vast amount of interesting information regarding human action/activities. In this thesis, we approach the problem of extracting this information and understanding human motion from a computer vision perspective. We propose solutions for two distinct scenarios, ordered from simple to complex. In the first scenario, we deal with the problem of single action recognition in relatively simple settings. We believe that human pose encapsulates many useful clues for recognizing the ongoing action, and we can represent this shape information for 2D single actions in very compact forms, before going into details of complex modeling. We show that high-accuracy single human action recognition is possible 1) using spatial oriented histograms of rectangular regions when the silhouette is extractable, 2) using the distribution of boundary-fitted lines when the silhouette information is missing. We demonstrate that, inside videos, we can further improve recognition accuracy by means of adding local and global motion information. We also show that within a discriminative framework, shape information is quite useful even in the case of human action recognition in still images. Our second scenario involves recognition and retrieval of complex human activities within more complicated settings, like the presence of changing background and viewpoints. We describe a method of representing human activities in 3D that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across time and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. Our models of short time scale limb behaviour are built using labelled motion capture set. Our query language makes use of finite state automata and requires simple text encoding and no visual examples. We show results for a large range of queries applied to a collection of complex motion and activity. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by some important changes of clothing.en_US
dc.description.statementofresponsibilityİkizler, Nazlıen_US
dc.format.extentxx, 121 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHuman motionen_US
dc.subjectclassificationen_US
dc.subjectimage and video processingen_US
dc.subjectactivity retrievalen_US
dc.subjectactivity recognitionen_US
dc.subjectaction recognitionen_US
dc.subject.lccTA1650 .I55 2008en_US
dc.subject.lcshOptical pattern recognition.en_US
dc.subject.lcshComputer vision.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshBody, Human--Computer simulation.en_US
dc.titleUnderstanding human motion : recognition and retrieval of human activitiesen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US


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