Attention! A Lightweight 2D Hand Pose Estimation Approach
This work addresses vision-based hand pose estimation for HCI applications, offering an incremental improvement in efficiency for embedded deployment.
The paper tackles 2D hand pose estimation for human-computer interaction by proposing a lightweight CNN with a self-attention module, achieving deployment on embedded systems with only 1.9 million parameters.
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.