Video motion capture using feature tracking and skeleton reconstruction

In the domain of computer vision, there exists a very wide application for the research of human motion capture. This paper proposes a new approach to do motion capture in video. It is composed of image sequence based tracking of human feature points and the reconstruction of three dimension (3D) motion skeleton. First, we track every part of human body from top to bottom on the basis of a human model. The Kalman filter and a morph-block similarity algorithm based on subpixel are used. Then we do camera calibration using the line correspondences between the 3D model and the image. Finally the 3D motion skeleton is established by using the model knowledge. This approach does not aim at a given mode of human motion. Rather, it analyzes large motion from frame to frame in complex, variational background, and sets up a 3D motion skeleton under the perspective projection. We also present the experimental result at the end of the paper