Full Text:
<2516>
CLC number: TP391.4
On-line Access:
Received: 1999-12-08
Revision Accepted: 1999-12-29
Crosschecked: 0000-00-00
Cited: 0
Clicked: 5175
VIDEO MOTION CAPTURE IN VBA--VIDEO-BASED ANIMATION
| |||||||||||||
PAN Yun-he, ZHUANG Yue-ting, LIU Xiao-ming. VIDEO MOTION CAPTURE IN VBA--VIDEO-BASED ANIMATION[J]. Journal of Zhejiang University Science A, 2000, 1(1): 1-7.
@article{title="VIDEO MOTION CAPTURE IN VBA--VIDEO-BASED ANIMATION",
author="PAN Yun-he, ZHUANG Yue-ting, LIU Xiao-ming",
journal="Journal of Zhejiang University Science A",
volume="1",
number="1",
pages="1-7",
year="2000",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2000.0001"
}
%0 Journal Article
%T VIDEO MOTION CAPTURE IN VBA--VIDEO-BASED ANIMATION
%A PAN Yun-he
%A ZHUANG Yue-ting
%A LIU Xiao-ming
%J Journal of Zhejiang University SCIENCE A
%V 1
%N 1
%P 1-7
%@ 1869-1951
%D 2000
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2000.0001
TY - JOUR
T1 - VIDEO MOTION CAPTURE IN VBA--VIDEO-BASED ANIMATION
A1 - PAN Yun-he
A1 - ZHUANG Yue-ting
A1 - LIU Xiao-ming
J0 - Journal of Zhejiang University Science A
VL - 1
IS - 1
SP - 1
EP - 7
%@ 1869-1951
Y1 - 2000
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2000.0001
Abstract: Computer vision has very wide application in human motion capture research. 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 the three-dimension(3D) motion skeleton. First, every part of the human body from top to bottom is tracked on the basis of a human model. The image difference and a morph-block similarity algorithm based on subpixels are used. Then camera calibration is done using the line correspondences between the 3D model and the image. Finally the 3D motion skeleton is established by use of the model knowledge. This approach does not aim at a given mode of human motion. Rather, it analyzes large scale motion from frame to frame in complex, variational background, and sets up a 3D motion skeleton in the perspective projection. The experiment results are presented at the end of the paper.
[1]Aggarwal, J. K., Cai, Q., 1997. Human Motion Analysis:A Review, In: Proc. of the IEEE Nonrigid and Articulated Motion Workshop. Piscataway, NJ,USA, p.90-102.
[2]Akita, K., 1984. Image sequence analysis of real world human motion. Pattern Recognition, 17(1):73-83.
[3]Bharatkumar, A. G., Daigle, K. E., Pandy, M. G., et al., 1994. Low limb kinematics of human walking with the medial axis transformation. In: Proc. of IEEE Workshop on Motion of No-Rigid and Articulated Objects. Austin,TX,USA. p.70-76.
[4]Bregler, C., Malik, J., 1998. Video Motion Capture, In: Proceeding of SIGGRAPH 98. USA.
[5]Chen, Z., Lee, H., 1992. Knowledge-guided visual perception of 3D human gait from a single image sequence, IEEE Trans. On Sys. Man. and Cybernetics, 22(2):336-342.
[6]Hogg, D., 1983. A program to see a walking person. Image Vision Computing, 5(20):28-42.
[7]Huang, T. S., Netravali, A. N.,1994. Motion and structure from feature correspondences: A review. Proceedings of The IEEE, 82(2):252-268.
[8]Ma Songde, and Zhang Zhengyou, 1998. Computer Vision: Compute Theory and Arithmetic Foundation. Science Press, Beijing, p.60-68.
[9]Rohr, K., 1993. Incremental recogniton of pedestrians from image sequences. In: Proc. IEEE Conf. Comput. Vision and Pattern Recogn., Allerton Press, New York,USA. p.8-13.
[10]Rourke, J.O., Badler, N.I., 1980. Model-based image analysis of human motion using constraint propagation. IEEE PAMI,2(6):522-536.
Open peer comments: Debate/Discuss/Question/Opinion
<1>