Manual Annotations on Depth Maps for Human Pose Estimation

Abstract

Few works tackle the Human Pose Estimation on depth maps. Moreover, these methods usually rely on automatically annotated datasets, and these annotations are often imprecise and unreliable, limiting the achievable accuracy using this data as ground truth. For this reason, in this paper we propose an annotation refinement tool of human poses, by means of body joints, and a novel set of fine joint annotations for the Watch-n-Patch dataset, which has been collected with the proposed tool. Furthermore, we present a fully-convolutional architecture that performs the body pose estimation directly on depth maps. The extensive evaluation shows that the proposed architecture outperforms the competitors in different training scenarios and is able to run in real-time.

Recommended citation

@inproceedings{deusanio2019manual,
  title={Manual Annotations on Depth Maps for Human Pose Estimation},
  author={D’Eusanio, Andrea and Pini, Stefano and Borghi, Guido and Vezzani, Roberto and Cucchiara, Rita},
  booktitle={International Conference on Image Analysis and Processing},
  pages={233--244},
  year={2019},
  organization={Springer}
}