ML Anthology

A Regression-Based Approach to Recover Human Pose from Voxel Data

Laetitia Gond, Patrick Sayd, Thierry Chateau, Michel Dhome
ICCVW 2009 pp. 1012-1019
doi:10.1109/ICCVW.2009.5457593 /iccvw/2009/gond2009iccvw-regressionbased/

Abstract

This paper deals with human body pose recovery from multiple cameras, which is a key task in monitoring of human activity. This regression-based approach relies on a 3D description of a body voxel reconstruction, combined with a decomposition of the estimation, which allows to recover a wide range of poses using synthetic training data. The precision of the proposed shape descriptor is quantitatively evaluated on synthetic data for a ground truth comparison, while the effectiveness of the whole system is qualitatively demonstrated on various real sequences.

ICCVW Semantic Scholar

Cite

Text

Gond et al. "A Regression-Based Approach to Recover Human Pose from Voxel Data." IEEE/CVF International Conference on Computer Vision Workshops, 2009. doi:10.1109/ICCVW.2009.5457593

Markdown

[Gond et al. "A Regression-Based Approach to Recover Human Pose from Voxel Data." IEEE/CVF International Conference on Computer Vision Workshops, 2009.](https://mlanthology.org/iccvw/2009/gond2009iccvw-regressionbased/) doi:10.1109/ICCVW.2009.5457593

BibTeX

@inproceedings{gond2009iccvw-regressionbased,
  title     = {{A Regression-Based Approach to Recover Human Pose from Voxel Data}},
  author    = {Gond, Laetitia and Sayd, Patrick and Chateau, Thierry and Dhome, Michel},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2009},
  pages     = {1012-1019},
  doi       = {10.1109/ICCVW.2009.5457593},
  url       = {https://mlanthology.org/iccvw/2009/gond2009iccvw-regressionbased/}
}