Hand gestures play a significant role in human interactions where non-verbal
intentions, thoughts and commands are conveyed. In Human-Robot Interaction
(HRI), hand gestures offer a similar and efficient medium for conveying clear
and rapid directives to a robotic agent. However, state-of-the-art vision-based
methods for gesture recognition have been shown to be effective only up to a
user-camera distance of seven meters. Such a short distance range limits
practical HRI with, for example, service robots, search and rescue robots and
drones. In this work, we address the Ultra-Range Gesture Recognition (URGR)
problem by aiming for a recognition distance of up to 25 meters and in the
context of HRI. We propose a novel deep-learning framework for URGR using
solely a simple RGB camera. First, a novel super-resolution model termed HQ-Net
is used to enhance the low-resolution image of the user. Then, we propose a
novel URGR classifier termed Graph Vision Transformer (GViT) which takes the
enhanced image as input. GViT combines the benefits of a Graph Convolutional
Network (GCN) and a modified Vision Transformer (ViT). Evaluation of the
proposed framework over diverse test data yields a high recognition rate of
98.1%. The framework has also exhibited superior performance compared to human
recognition in ultra-range distances. With the framework, we analyze and
demonstrate the performance of an autonomous quadruped robot directed by human
gestures in complex ultra-range indoor and outdoor environments.

通过使用简单的 RGB 相机，提出了一种新颖的深度学习框架，以解决人与机器人交互中的超远距离手势识别问题，该框架在复杂的室内和室外环境中指导一个自主四足机器人的表现优于人类识别的性能。