Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception

Hankyul Baek, Yoo Jeong, Ha, Minjae Yoo, Soyi Jung, Joongheon Kim

Submitted on 7 November 2022


In modern on-driving computing environments, many sensors are used for context-aware applications. This paper utilizes two deep learning models, U-Net and EfficientNet, which consist of a convolutional neural network (CNN), to detect hand gestures and remove noise in the Range Doppler Map image that was measured through a millimeter-wave (mmWave) radar. To improve the performance of classification, accurate pre-processing algorithms are essential. Therefore, a novel pre-processing approach to denoise images before entering the first deep learning model stage increases the accuracy of classification. Thus, this paper proposes a deep neural network based high-performance nonlinear pre-processing method.


Comment: 4 pages, 7 figures

Subjects: Electrical Engineering and Systems Science - Signal Processing; Computer Science - Computer Vision and Pattern Recognition