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

TL;DR

This paper introduces a deep neural network-based nonlinear pre-processing method to denoise mmWave radar images, enhancing gesture classification accuracy in autonomous driving environments.

Contribution

It presents a novel deep learning pre-processing approach that improves gesture recognition accuracy by effectively denoising Range Doppler Map images before classification.

Findings

Improved gesture classification accuracy with the proposed pre-processing.

Effective noise removal from mmWave radar images.

Enhanced performance of CNN-based gesture recognition.

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.