3D Ballet Motion Tracking from Shape Context by Using Differential Regression

DOI

10.2991/icmt-13.2013.59How to use a DOI?

Keywords

3D Human motion, Differential Regression, Shape Context

Abstract

An improved method, differential regression, is proposed. It is a more powerful discriminative approach for human pose estimation. The proposed methods investigate 3D body pose reconstruction by learning a simply regression between state vector differences and observation vector. A simulation model is established on a 57 dimensions human skeleton. The comparison experiments between proposed method and traditional regression are carried out by using a sequence of images on Ballet dancing. The calculated deviations are greatly reduced by 50%.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tong Minglei
AU  - Han Hong
AU  - Chen Shudong
PY  - 2013/11
DA  - 2013/11
TI  - 3D Ballet Motion Tracking from Shape Context by Using Differential Regression
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 479
EP  - 486
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.59
DO  - 10.2991/icmt-13.2013.59
ID  - Minglei2013/11
ER  -