SuperResolution Radar Gesture Recognitio

TL;DR

This paper presents a deep learning-based super-resolution method for low-resolution radar data to enable accurate in-cabin gesture recognition, improving vehicle safety by overcoming limitations of optical systems under challenging conditions.

Contribution

It introduces a novel combination of signal processing and deep learning to enhance radar resolution for real-time gesture recognition in vehicles.

Findings

High gesture recognition accuracy achieved with low-resolution radars.

Effective super-resolution of radar data improves spatial resolution.

Method operates in real-time for practical in-cabin use.

Abstract

"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible." Driver's interaction with a vehicle via automatic gesture recognition is expected to enhance driving safety by decreasing driver's distraction. Optical and infrared-based gesture recognition systems are limited by occlusions, poor lighting, and varying thermal conditions and, therefore, have limited performance in practical in-cabin applications. Radars are insensitive to lighting or thermal conditions and, therefore, are more suitable for in-cabin applications. However, the spatial resolution of conventional radars is insufficient for accurate gesture recognition. The main objective of this research is to derive an accurate gesture recognition approach using low-resolution radars with deep learning-based super-resolution processing. The main idea is to reconstruct high-resolution information from the radar's low-resolution measurements. The major challenge is the derivation of the real-time processing approach. The proposed approach combines conventional signal processing and deep learning methods. The radar echoes are arranged in 3D data cubes and processed using a super-resolution model to enhance range and Doppler resolution. The FFT is used to generate the range-Doppler maps, which enter the deep neural network for efficient gesture recognition. The preliminary results demonstrated the proposed approach's efficiency in achieving high gesture recognition performance using conventional low-resolution radars.