1 Introduction
Human motion capture and analysis has applications in different fields such as kinesiology, biomechanics, surveillance, human-computer interaction, animation, and videogames. There is a correspondingly large body of literature on human motion analysis and pose estimation from video data. However, the requirements in terms of the detail of pose parameters and accuracy in estimation vary from application to application, as does the form of the available input data. Surveillance applications, for instance, usually require just the location of the subject or an approximate estimate of human pose from a single video stream, whereas biomechanical applications require accurate pose estimates of different joint angles from images obtained using multiple video cameras. The most common methods for accurate capture of 3D human movement require attachment of markers, fixtures, or sensors to body segments. These methods are invasive; i.e., they encumber the subject, hinder movement, and require subject preparation time. Biomechanical and clinical applications [1], [2] require the accurate capture of normal and pathological human movement without the artifacts associated with current state-of-the-art marker-based motion capture techniques. A markerless motion capture system using multiple video streams therefore possesses several advantages over marker-based systems and is highly desirable.