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dc.contributor.advisorDuygulu, Pınaren_US
dc.contributor.authorKurt, Mehmet Canen_US
dc.date.accessioned2016-01-08T18:15:44Z
dc.date.available2016-01-08T18:15:44Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11693/15260
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2011.en_US
dc.descriptionIncludes bibliographical references leaves 44-46.en_US
dc.description.abstractThis thesis utilizes a key-pose based representation to recognize human actions in videos. We believe that the pose of the human figure is a powerful source for describing the nature of the ongoing action in a frame. Each action can be represented by a unique set of frames that include all the possible spatial configurations of the human body parts throughout the time the action is performed. Such set of frames for each action referred as “key poses” uniquely distinguishes that action from the rest. For extracting “key poses”, we define a similarity value between the poses in a pair of frames by using the lines forming the human figure along with a shape matching method. By the help of a clustering algorithm, we group the similar frames of each action into a number of clusters and use the centroids as “key poses” for that action. Moreover, in order to utilize the motion information present in the action, we include simple line displacement vectors for each frame in the “key poses” selection process. Experiments on Weizmann and KTH datasets show the effectiveness of our key-pose based approach in representing and recognizing human actions.en_US
dc.description.statementofresponsibilityKurt, Mehmet Canen_US
dc.format.extentxi, 46 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHuman motionen_US
dc.subjectAction recognitionen_US
dc.subjectKey-poseen_US
dc.subjectPose similarityen_US
dc.subjectPose matchingen_US
dc.subject.lccQP301 .K87 2011en_US
dc.subject.lcshHuman locomotion--Computer simulation.en_US
dc.subject.lcshBody, Human--Computer simulation.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshComputer simulation.en_US
dc.subject.lcshDigital computer vision.en_US
dc.subject.lcshPattern recognition systems.en_US
dc.titleA key-pose based representation for human action recognitionen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB134192


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