This paper presents a novel approach for ultra-range gesture recognition, addressing Human-Robot Interaction (HRI) challenges over extended distances. By leveraging human gestures in video data, we propose the Temporal-Spatiotemporal Fusion Network (TSFN) model that surpasses the limitations of current methods, enabling robots to understand gestures from long distances. With applications in service robots, search and rescue operations, and drone-based interactions, our approach enhances HRI in expansive environments. Experimental validation demonstrates significant advancements in gesture recognition accuracy, particularly in prolonged gesture sequences.

翻译：本文提出了一种用于超远距离手势识别的新方法，以解决远距离人机交互中的挑战。通过利用视频数据中的人体手势，我们提出了时空融合网络模型，该模型克服了现有方法的局限性，使机器人能够理解远距离手势。我们的方法在服务机器人、搜救行动和基于无人机的交互等应用中，增强了广阔环境中的人机交互能力。实验验证表明，该方法在手势识别准确率方面取得了显著进步，尤其是在长时序手势识别任务中。