Video based human motion capture

Proposes a new approach to capture human motion in video. This approach does not aim at a given human motion mode, but instead analyzes large-scale motion from frame to frame in a complex variational background and sets up a 3D motion skeleton under perspective projection. This approach is composed of two steps. First, we track every part of a human body from top to bottom on the basis of a human model. Then we perform a camera calibration using the line correspondences between the 3D model and the image, and establish the 3D motion skeleton by using the human model knowledge. The experimental results are presented at the end of the paper.

[1]  Takeo Kanade,et al.  Model-based tracking of self-occluding articulated objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[2]  Yueting Zhuang,et al.  Video motion capture using feature tracking and skeleton reconstruction , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[3]  Thomas S. Huang,et al.  Motion and structure from feature correspondences: a review , 1994, Proc. IEEE.

[4]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Other Conferences.

[5]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[6]  Koichiro Akita,et al.  Image sequence analysis of real world human motion , 1984, Pattern Recognit..

[7]  Hsi-Jian Lee,et al.  Knowledge-guided visual perception of 3-D human gait from a single image sequence , 1992, IEEE Trans. Syst. Man Cybern..

[8]  J. O'Rourke,et al.  Model-based image analysis of human motion using constraint propagation , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.