The intimate relationship between human walking and running lies within the skeleto-muscular structure. This is expressed as a mapping that can transform computer vision derived gait signatures from running to walking and vice versa, for purposes of deployment in gait as a biometric or for animation in computer graphics. The computer vision technique can extract leg motion by temporal template matching with a model defined by forced coupled oscillators as the basis. The (biometric) signature is derived from Fourier analysis of the variation in the motion of the thigh and lower leg. ln fact, the mapping between these gait modes clusters better than the original signatures (of which running is the more potent) and can be used for recognition purposes alone, or to buttress both of the signatures. Moreover, the two signatures can be made invariant to gait mode by using the new mapping. There is much research in analysing human gait via computer vision, e.g. human motion (walking) tracking [I] . However, the potential of both walking and running gait [8, 9] as cues for person identification has only been explored recently. ' And these model-based approaches include analysis of the thigh and lower leg motion. Nevertheless, the relationship between human walking and running (in computer vision) remains imperfectly understood. Due to the fact that these two gaits are derived from the same skeletomuscular system, there must exist some correlation between them. Perhaps what makes it seem unfeasible is that human gait is not only a physiological property, but also a complex behavioural [IO] characteristic. That is, we learn how to walk and run when growing up and individuals with similar physiological traits may have their own particular way of walking and running. An understanding of the relationship between human walking and running is essential not only for further improving the existing automatic person recognition system using gait, but also as a foundation for other studies e.g. biomechanics, robotics and computer graphics animation. To determine a relationship between these two biomechanically distinct gaits, we deployed the first analytical human gait model developed earlier [9] which is invariant to walking and running, to automatically extract the lower limbs' motion using computer vision. This paper concentrates on the generic formulae to describe the relationship of the two different gaits. The mapping is based on the idea of phase modulation. It is then used to create signatures that are invariant to walking and running, and enhance the original gait signature. The analysis here has been applied to the largest database of its kind, comprising 20 subjects who are walking and running. The edge maps (Fig. la) obtained by applying.the Sobel edge operator with a threshold on the horizontal component are used in an evidence gathering process to extract the lower limb's motion. The angles of rotation when walking and running are extracted automatically via a 2-pass evidence gathering technique with an analytical model [9] as the underlying template. This model includes the vertical motion of the hip (Eq. l), the thigh rotation (Eq. 2) and the lower leg rotation (Eq. 3). Note that only the hip's vertical motion is included since the subjects are filmed walking and running on a motorised treadmill, and the resolution of the images used during the feature extraction is relatively low. Hence, the horizontal motion is less significant. The hip's relative vertical motion, S,,, is given as a function of time t by S,(t) = A,. sin(2wYt + 0, ) where A, is the amplitude of the vertical oscillation and $? is the phase shifi. The frequency is twice the fundamental, 9, since one gait cycle consists of two steps. The thigh rotation, Bn and the lower leg rotation, Ox, are derived from forced coupled oscillators joined in series where the upper and lower penduli model Br and BK, respectively, (see Fig. 1 b) as described by er = A cos(ogr + $ r ) + B sin(w+ +e,) + M,
Later, they further explored the intimate relationship between walking and running that was expressed as a mapping based on the idea of phase modulation REF .
On the relationship of human walking and running: automatic person identification by gait
Object recognition supported by user interaction for service robots
Object recognition supported by user interaction for service robots
Computer Science