Human activity classification based on gait and support vector machines

Presented 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 elect 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.