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.
@article{arxiv.2211.03502,
title = {Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception},
author = {Hankyul Baek and Yoo Jeong and Ha and Minjae Yoo and Soyi Jung and Joongheon Kim},
journal= {arXiv preprint arXiv:2211.03502},
year = {2022}
}