James J. Little
Department of Computer Science
University of British Columbia
Jeffrey E. Boyd
Department of Electrical and Computer Engineering
University of California at San Diego
Abstract
The image flow of a moving figure varies both spatially and temporally. We develop a model-free description of instantaneous motion, the shape of motion, that varies with the type of moving figure and the type of motion. We use that description to recognize individuals by their gait, discriminating them by periodic variation in the shape of their motion. For each image in a sequence, we derive dense optical flow, (u(x, y), v(x, y)). Scale-independent scalar features of each flow, based on moments of the moving point weighted by |u|, |v|, or |(u, v)|, characterize the spatial distribution of the flow.We then analyze the periodic structure of these sequences of scalars. The scalar sequences for an image sequence have the same fundamental period but differ in phase, which is a phase feature for each signal. Some phase features are consistent for one person and show significant statistical variation among persons. We use the phase feature vectors to recognize individuals by the shape of their motion. As few as three features out of the full set of twelve lead to excellent discrimination.
Keywords: action recognition, gait recognition, motion features, optic flow, motion energy, spatial frequency, analysis