Object recognition, commonly performed by a camera, is a fundamental
requirement for robots to complete complex tasks. Some tasks require
recognizing objects far from the robot's camera. A challenging example is
Ultra-Range Gesture Recognition (URGR) in human-robot interaction where the
user exhibits directive gestures at a distance of up to 25~m from the robot.
However, training a model to recognize hardly visible objects located in
ultra-range requires an exhaustive collection of a significant amount of
labeled samples. The generation of synthetic training datasets is a recent
solution to the lack of real-world data, while unable to properly replicate the
realistic visual characteristics of distant objects in images. In this letter,
we propose the Diffusion in Ultra-Range (DUR) framework based on a Diffusion
model to generate labeled images of distant objects in various scenes. The DUR
generator receives a desired distance and class (e.g., gesture) and outputs a
corresponding synthetic image. We apply DUR to train a URGR model with
directive gestures in which fine details of the gesturing hand are challenging
to distinguish. DUR is compared to other types of generative models showcasing
superiority both in fidelity and in recognition success rate when training a
URGR model. More importantly, training a DUR model on a limited amount of real
data and then using it to generate synthetic data for training a URGR model
outperforms directly training the URGR model on real data. The synthetic-based
URGR model is also demonstrated in gesture-based direction of a ground robot.

对于机器人完成复杂任务来说，对象识别是基本要求。在人机交互的超远距离姿势识别方面，我们提出了基于扩散模型的超远距离扩散（DUR）框架，能够生成各种场景中远距离对象的标记图像，并且在训练超远距离姿势识别模型时表现出了优越性。

超远距离条件下训练目标识别模型的扩散式数据生成器

A Diffusion-based Data Generator for Training Object Recognition Models  in Ultra-Range Distance

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。