Robust Dynamic Gesture Recognition at Ultra-Long Distances

TL;DR

This paper introduces a novel gesture recognition model capable of accurately identifying dynamic hand gestures at ultra-long distances up to 28 meters, enhancing human-robot interaction in diverse environments.

Contribution

The paper presents the SlowFast-Transformer (SFT) model with a distance-weighted loss function, enabling effective gesture recognition at ultra-range distances, surpassing existing methods.

Findings

Achieved 95.1% recognition accuracy on ultra-range gesture dataset.

Demonstrated robustness against environmental noise and low resolution.

Enabled natural robot guidance from 28 meters away.

Abstract

Dynamic hand gestures play a crucial role in conveying nonverbal information for Human-Robot Interaction (HRI), eliminating the need for complex interfaces. Current models for dynamic gesture recognition suffer from limitations in effective recognition range, restricting their application to close proximity scenarios. In this letter, we present a novel approach to recognizing dynamic gestures in an ultra-range distance of up to 28 meters, enabling natural, directive communication for guiding robots in both indoor and outdoor environments. Our proposed SlowFast-Transformer (SFT) model effectively integrates the SlowFast architecture with Transformer layers to efficiently process and classify gesture sequences captured at ultra-range distances, overcoming challenges of low resolution and environmental noise. We further introduce a distance-weighted loss function shown to enhance learning and improve model robustness at varying distances. Our model demonstrates significant performance improvement over state-of-the-art gesture recognition frameworks, achieving a recognition accuracy of 95.1% on a diverse dataset with challenging ultra-range gestures. This enables robots to react appropriately to human commands from a far distance, providing an essential enhancement in HRI, especially in scenarios requiring seamless and natural interaction.