Our goal is to describe the motion of a moving human figure in order to
recognize individuals by variation in the characteristics of the motion
description. We begin with a short sequence of images of a moving
figure, taken by a static camera, and derive dense optical flow data,
(*u*(*x,y*),*v*(*x,y*)), for the sequence. We
determine a range of scale-independent scalar features of each flow
image that characterize the spatial distribution of the flow, i.e., the
shape of the motion. The scalars are based on various moments of the
set of moving points. To characterize the shape of the motion, not the
shape of moving points, the points are weighted by |*u*|,
|*v*|, or |(*u,v*)|.

We then analyze the periodic structure of these sequences of scalars. All of these sequences share the same fundamental period of the gait, but they differ in phase. Although there are several regularities in the relative phases of the signals, some of the phases show significant statistical variation. Therefore, we are able to use vectors of phase measurements derived for each image sequence to recognize individuals by the shape of their motion.

The representation is model-free, and therefore could be used to characterize the motion of other non-rigid bodies.

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