Contours, Optic Flow, and Prior Knowledge: Cues for Capturing 3D Human Motion in Videos

Human 3D motion tracking from video is an emerging research field with many applications demanding highly detailed results. This chapter surveys a high quality generative method, which employs the person’s silhouette extracted from one or multiple camera views for fitting an a-priori given 3D body surface model. A coupling between pose estimation and contour extraction allows for reliable tracking in cluttered scenes without the need of a static background. The optic flow computed between two successive frames is used for pose prediction. It improves the quality of tracking in case of fast motion and/or low frame rates. In order to cope with unreliable or insufficient data, the framework is further extended by the use of prior knowledge on static joint angle configurations.