Human Activity Classification Based on Gait and Support Vector Machines

Abstract

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 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.