Abstract
Human gait analysis is important in health and sport management, medical research, and biometrics. Gait analysis is predominantly based on a motion capture system and video data. Video data-based gait analysis is useful to observe gait motion in a field study. However, many video-based gait analysis methods use lateral-view gait motion. Video data filmed from a frontal view are difficult to analyze, due to the subject being too close to the camera. In this study, we focus on the shape scale-changing in the frontal-view human gait. We estimate scale parameters using statistical registration and modeling with video data. To demonstrate the effectiveness of our method, we apply our model to normal and abnormal gait analysis. Our model performs well in terms of scale estimation and human gait analysis.
