论文标题
使用可变形变压器的MM波雷达手形分类
mm-Wave Radar Hand Shape Classification Using Deformable Transformers
论文作者
论文摘要
提出了一种新型实时的，实时的，MM波雷达的静态手形分类算法和实现。该方法使用60 GHz雷达作为传感器输入找到了低成本和隐私敏感的无触摸控制技术的几种应用。与基于先前的范围多普勒图像的2D分类解决方案相反，我们的方法将原始雷达数据转换为3D稀疏的笛卡尔点云。使用可变形的变压器使用的3D雷达神经网络模型可显着超过先前方法设置的性能结果，该方法通过使用自定义信号处理或应用范围内的通用技术在范围内应用范围fft fft fft图像。实验是使用现成的雷达传感器在内部收集的数据集上进行的。
A novel, real-time, mm-Wave radar-based static hand shape classification algorithm and implementation are proposed. The method finds several applications in low cost and privacy sensitive touchless control technology using 60 Ghz radar as the sensor input. As opposed to prior Range-Doppler image based 2D classification solutions, our method converts raw radar data to 3D sparse cartesian point clouds.The demonstrated 3D radar neural network model using deformable transformers significantly surpasses the performance results set by prior methods which either utilize custom signal processing or apply generic convolutional techniques on Range-Doppler FFT images. Experiments are performed on an internally collected dataset using an off-the-shelf radar sensor.
