论文标题
使用毫米波雷达的远程手势识别
Long-Range Gesture Recognition Using Millimeter Wave Radar
论文作者
论文摘要
基于毫米的手势识别技术提供了良好的人类计算机互动（HCI）体验。先前的工作着重于近距离识别，但范围延伸的距离不足，即他们无法识别远离相当大的噪声动作一米以上的手势。在本文中，我们设计了一种远程手势识别模型，该模型利用了一种新型的数据处理方法和定制的人工卷积神经网络（CNN）。首先，我们将手势分解为多个反射点，并提取它们的时空特征，这些特征描绘了手势细节。其次，我们设计了一个CNN，以分别学习提取特征的变化模式并输出识别结果。我们通过在商品MMWave雷达上实施彻底评估了我们提出的系统。此外，我们还提供更广泛的评估，以证明在几种现实世界中，所提出的系统是实用的。
Millimeter wave (mmWave) based gesture recognition technology provides a good human computer interaction (HCI) experience. Prior works focus on the close-range gesture recognition, but fall short in range extension, i.e., they are unable to recognize gestures more than one meter away from considerable noise motions. In this paper, we design a long-range gesture recognition model which utilizes a novel data processing method and a customized artificial Convolutional Neural Network (CNN). Firstly, we break down gestures into multiple reflection points and extract their spatial-temporal features which depict gesture details. Secondly, we design a CNN to learn changing patterns of extracted features respectively and output the recognition result. We thoroughly evaluate our proposed system by implementing on a commodity mmWave radar. Besides, we also provide more extensive assessments to demonstrate that the proposed system is practical in several real-world scenarios.
