Dop-DenseNet: Densely Convolutional Neural Network-Based Gesture Recognition Using a Micro-Doppler Radar
Hand gesture recognition is an efficient and practical solution for the non-contact human–machine interaction in smart devices. To date, vision-based methods are widely used in this research area, but they are susceptible to light conditions. To address this issue, radar-based gesture recognition us...
Main Authors: | Hai Le, Van-Phuc Hoang, Van Sang Doan, Dai Phong Le |
---|---|
Format: | Article |
Language: | English |
Published: |
The Korean Institute of Electromagnetic Engineering and Science
2022-05-01
|
Series: | Journal of Electromagnetic Engineering and Science |
Subjects: | |
Online Access: | http://jees.kr/upload/pdf/jees-2022-3-r-95.pdf |
Similar Items
-
Hand Gesture Recognition Using FSK Radar Sensors
by: Kimoon Yang, et al.
Published: (2024-01-01) -
Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures
by: Zhi Zhou, et al.
Published: (2017-12-01) -
Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning
by: Seungeon Song, et al.
Published: (2021-06-01) -
Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks
by: Norah Alnaim, et al.
Published: (2020-04-01) -
Clustering-Driven DGS-Based Micro-Doppler Feature Extraction for Automatic Dynamic Hand Gesture Recognition
by: Chengjin Zhang, et al.
Published: (2022-11-01)