Dynamic Gesture Recognition in Ultra-Range Distance for Effective Human-Robot Interaction

TL;DR

This paper introduces the Temporal-Spatiotemporal Fusion Network (TSFN), a novel model that significantly improves ultra-range gesture recognition for human-robot interaction over extended distances, enabling more effective communication in large environments.

Contribution

The paper presents a new TSFN model that surpasses existing methods in ultra-range gesture recognition, addressing challenges in long-distance HRI scenarios.

Findings

Enhanced recognition accuracy over long distances

Effective in prolonged gesture sequences

Applicable to various HRI environments

Abstract

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.