Dynamic Gesture Recognition in Ultra-Range Distance for Effective Human-Robot Interaction
It addresses a domain-specific challenge for service robots, search and rescue, and drone interactions, with incremental improvements over existing methods.
This paper tackles the problem of recognizing human gestures from ultra-range distances to improve Human-Robot Interaction, achieving significant advancements in accuracy for prolonged gesture sequences.
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.