DeepPose: Human Pose Estimation via Deep Neural Networks

CVPR 2014

doi:10.1109/CVPR.2014.214 /cvpr/2014/toshev2014cvpr-deeppose/

Abstract

We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formulation which capitalizes on recent advances in Deep Learning. We present a detailed empirical analysis with state-of-art or better performance on four academic benchmarks of diverse real-world images.

PDF CVPR Semantic Scholar

Cite

Text

Toshev and Szegedy. "DeepPose: Human Pose Estimation via Deep Neural Networks." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.214

Markdown

[Toshev and Szegedy. "DeepPose: Human Pose Estimation via Deep Neural Networks." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/toshev2014cvpr-deeppose/) doi:10.1109/CVPR.2014.214

BibTeX

@inproceedings{toshev2014cvpr-deeppose,
  title     = {{DeepPose: Human Pose Estimation via Deep Neural Networks}},
  author    = {Toshev, Alexander and Szegedy, Christian},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.214},
  url       = {https://mlanthology.org/cvpr/2014/toshev2014cvpr-deeppose/}
}