实现28米外精准手势识别，让远距离人机交互更自然。
Robust Dynamic Gesture Recognition at Ultra-Long Distances
- 结合SlowFast与Transformer结构，处理远距离低分辨率手势序列。
- 在28米超远距离下达到95.1%识别准确率。
- 适合户外及复杂环境下的机器人远程指挥场景。
动态手势在人机交互中起着关键作用，可避免复杂界面。现有手势识别模型有效识别范围有限，仅适用于近距离场景。本文提出一种新方法，可在高达28米的超远距离实现动态手势识别，支持室内外环境中自然、直接的机器人引导。所提出的SlowFast-Transformer（SFT）模型将SlowFast架构与Transformer层融合，高效处理超远距离捕捉的手势序列，克服低分辨率和环境噪声挑战。我们还引入距离加权损失函数，提升模型在不同距离下的学习效果与鲁棒性。在包含挑战性远距离手势的多样化数据集上，模型识别准确率达95.1%，显著优于现有框架。该技术使机器人能远距离响应人类指令，极大提升了人机交互的自然性与无缝性。
原文摘要 · Abstract (English)
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.
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