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

• URL: http://arxiv.org/abs/2211.03502v1
• Date: Mon, 7 Nov 2022 12:42:13 GMT
• Title: Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception
• Authors: Hankyul Baek and Yoo Jeong (Anna) Ha and Minjae Yoo and Soyi Jung and Joongheon Kim
• Abstract summary: This paper utilizes two deep learning models, U-Net and EfficientNet, to detect hand gestures and remove noise in a millimeter-wave (mmWave) radar image. A novel pre-processing approach to denoise images before entering the first deep learning model stage increases the accuracy of classification.
• Score: 10.826849062116748
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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