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.
D'EUSANIO, A., Stefano Pini, Guido Borghi, Roberto Vezzani, Rita Cucchiara (2019). Manual Annotations on Depth Maps for Human Pose Estimation [10.1007/978-3-030-30642-7_21].
Manual Annotations on Depth Maps for Human Pose Estimation
Guido Borghi;Rita Cucchiara
2019
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.