The objective of human pose estimation is to estimate the locations of keypoints on the human body using a single image. Convolutional pose machines is one of the most popular pose estimation techniques that is based on deep learning with convolutional features. In this paper, we propose semantic pose machines, a pose estimation technique that enhances convolutional pose machines by utilizing a semantic segmentation heatmap in addition to convolutional features. Semantic segmentation methods leverage the success of object class recog-nition networks for the segmentation of important object classes, including people. We consider the CRF as RNN semantic seg-mentation approach to obtain a heatmap that is incorporated in the pose estimation process as an additional channel. Our results on the LEEDS dataset indicate improvements over the convolutional pose machines method.
Ying-Kai Huang, Andreas Savakis, "Semantic Pose Machines" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, 2018, pp 453-1 - 453-6, https://doi.org/10.2352/ISSN.2470-1173.2018.10.IMAWM-453