Human motion analysis via statistical motion processing and sequential change detection
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
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Moving light displays (MLDs) have been used extensively to study motion perception and perception of the human gait in particular. MLD perception is largely considered to be structural, i.e., perception depends on identification of human kinematic structure. However, recent work by Little and Boyd has shown that it is possible to recognize individual people, from their gaits, by non-structural means. They use global shape-of-motion features derived from optical flow in a sequence of gray-scale images. Our goal is to show that shape-of-motion features can be derived equally well from MLD images as from gray-scale images, and to compare the recent results obtained for shape-of-motion recognition with psychophysical observations about MLD perception. The implication is that non-structural shape-of-motion interpretation of gait can be applied to MLDs, allowing us to interpret significant MLD results in the context of a known algorithm. Our results shed light on the validity of shape-of-motion features from the psychophysical standpoint as well as suggest an alternative approach to understanding MLD perception. In particular, we find that characterizing movement in a gait may be treated as the sum of a set of moving points (if this is true then MLD lights need not be placed right at joints). Changes to a subset of the points affect the sum and consequently affect the perception of the whole.