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.
@article{arxiv.1312.4659,
title = {DeepPose: Human Pose Estimation via Deep Neural Networks},
author = {Alexander Toshev and Christian Szegedy},
journal= {arXiv preprint arXiv:1312.4659},
year = {2016}
}
Comments
IEEE Conference on Computer Vision and Pattern Recognition, 2014