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SYMMETRY IN HUMAN MOTION ANALYSIS: THEORY AND EXPERIMENTS

dc.contributor.advisorChellappa, Ramaen_US
dc.contributor.authorRan, Yangen_US
dc.date.accessioned2006-09-12T05:41:32Z
dc.date.available2006-09-12T05:41:32Z
dc.date.issued2006-06-27en_US
dc.identifier.urihttp://hdl.handle.net/1903/3760
dc.description.abstractVideo based human motion analysis has been actively studied over the past decades. We propose novel approaches that are able to analyze human motion under such challenges and apply them to surveillance and security applications. Part I analyses the cyclic property of human motion and presents algorithms to classify humans in videos by their gait patterns. Two approaches are proposed. The first employs the omputationally efficient periodogram, to characterize periodicity. In order to integrate shape and motion, we convert the cyclic pattern into a binary sequence using the angle between two legs when the toe-to-toe distance is maximized during walking. Part II further extends the previous approaches to analyze the symmetry in articulation within a stride. A feature that has been shown in our work to be a particularly strong indicator of the presence of pedestrians is the X-junction generated by bipedal swing of body limbs. The proposed algorithm extracts the patterns in spatio-temporal surfaces. In Part III, we present a compact characterization of human gait and activities. Our approach is based on decomposing an image sequence into x-t slices, which generate twisted patterns defined as the Double Helical Signature (DHS). It is shown that the patterns sufficiently characterize human gait and a class of activities. The features of DHS are: (1) it naturally codes appearance and kinematic parameters of human motion; (2) it reveals an inherent geometric symmetry (Frieze Group); and (3) it is effective and efficient for recovering gait and activity parameters. Finally, we use the DHS to classify activities such as carrying a backpack, briefcase etc. The advantage of using DHS is that we only need a small portion of 3D data to recognize various symmetries.en_US
dc.format.extent2285927 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleSYMMETRY IN HUMAN MOTION ANALYSIS: THEORY AND EXPERIMENTSen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentElectrical Engineeringen_US
dc.subject.pqcontrolledEngineering, Electronics and Electricalen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pquncontrolledHuman Motion Analysisen_US
dc.subject.pquncontrolledSurveillanceen_US
dc.subject.pquncontrolledComputer Visionen_US
dc.subject.pquncontrolledGeometric Group Theoryen_US


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