Vision based human pose estimation is an non-invasive technology for Human-Computer Interaction (HCI). Direct use of the hand as an input device provides an attractive interaction method, with no need for specialized sensing equipment, such as exoskeletons, gloves etc, but a camera. Traditionally, HCI is employed in various applications spreading in areas including manufacturing, surgery, entertainment industry and architecture, to mention a few. Deployment of vision based human pose estimation algorithms can give a breath of innovation to these applications. In this letter, we present a novel Convolutional Neural Network architecture, reinforced with a Self-Attention module that it can be deployed on an embedded system, due to its lightweight nature, with just 1.9 Million parameters. The source code and qualitative results are publicly available.
@article{arxiv.2001.08047,
title = {Attention! A Lightweight 2D Hand Pose Estimation Approach},
author = {Nicholas Santavas and Ioannis Kansizoglou and Loukas Bampis and Evangelos Karakasis and Antonios Gasteratos},
journal= {arXiv preprint arXiv:2001.08047},
year = {2020}
}