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Human Activity Classification Based on Gait and Support Vector Machines

dc.contributor.advisorChellappa, Ramaen_US
dc.contributor.authorDucao II, Amon Brigolien_US
dc.description.abstractPresented is a method to characterize human gait and to classify human activities using gait. Slices along the x-t dimension of a patio-temporal sequence are extracted to construct a gait double helical signature (gait DHS). A DHS pattern is a compact description that encodes the parameters of human gait and shows inherent symmetry in natural walking (without encumbered limb movement). The symmetry takes the form of Frieze groups, and differences in DHS symmetry can classify different activities. This thesis presents a method for extracting gait DHS, and how the DHS can be separable by activity. Then, a Support Vector Machine (SVM) n-class classifier is constructed using the Radial Basis Function (RBF) kernel, and the performance is measured on a set of data. The SVM is a classification tool based on learning from a training set, and fitting decision boundaries based on an output function. This thesis examines the effect of slicing at different heights of the body and shows the robustness of DHS to view angle, size, and direction of motion. Experiments using real video sequences are presented.en_US
dc.format.extent1329527 bytes
dc.titleHuman Activity Classification Based on Gait and Support Vector Machinesen_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

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