Machine Learning based In-Cabin Radar System for Passenger Monitoring System

Eugin Hyun; Yiung Seok Jin; Jieun Bae; Park Chi-Ho

doi:10.1109/VTC2023-Spring57618.2023.10200306

Machine Learning based In-Cabin Radar System for Passenger Monitoring System

Eugin Hyun, Yiung Seok Jin, Jieun Bae, Park Chi-Ho

Daegu Gyeongbuk Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we propose a passenger monitoring scheme using a 60GHz FMCW radar for in-cabin applications. Based on the 2D images using cloud point, we employed a 2D deep learning method. We found an average recognition rate at 96 %, which was one to five occupants in a vehicle.

Original language	English
Title of host publication	2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350311143
DOIs	https://doi.org/10.1109/VTC2023-Spring57618.2023.10200306
State	Published - 2023
Event	97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy Duration: 20 Jun 2023 → 23 Jun 2023

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2023-June
ISSN (Print)	1550-2252

Conference

Conference	97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Country/Territory	Italy
City	Florence
Period	20/06/23 → 23/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

in-cabin radar
passenger detection
passenger monitoring
radar cloud point
radar machine learning

Access to Document

10.1109/VTC2023-Spring57618.2023.10200306

Cite this

Hyun, E., Jin, Y. S., Bae, J., & Chi-Ho, P. (2023). Machine Learning based In-Cabin Radar System for Passenger Monitoring System. In 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings (IEEE Vehicular Technology Conference; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2023-Spring57618.2023.10200306

@inproceedings{8773e9a4fc2044d1ae0653ffe9ecb9a5,

title = "Machine Learning based In-Cabin Radar System for Passenger Monitoring System",

abstract = "In this paper, we propose a passenger monitoring scheme using a 60GHz FMCW radar for in-cabin applications. Based on the 2D images using cloud point, we employed a 2D deep learning method. We found an average recognition rate at 96 \%, which was one to five occupants in a vehicle.",

keywords = "in-cabin radar, passenger detection, passenger monitoring, radar cloud point, radar machine learning",

author = "Eugin Hyun and Jin, \{Yiung Seok\} and Jieun Bae and Park Chi-Ho",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 97th IEEE Vehicular Technology Conference, VTC 2023-Spring ; Conference date: 20-06-2023 Through 23-06-2023",

year = "2023",

doi = "10.1109/VTC2023-Spring57618.2023.10200306",

language = "English",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings",

}

Hyun, E, Jin, YS, Bae, J & Chi-Ho, P 2023, Machine Learning based In-Cabin Radar System for Passenger Monitoring System. in 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. IEEE Vehicular Technology Conference, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 97th IEEE Vehicular Technology Conference, VTC 2023-Spring, Florence, Italy, 20/06/23. https://doi.org/10.1109/VTC2023-Spring57618.2023.10200306

Machine Learning based In-Cabin Radar System for Passenger Monitoring System. / Hyun, Eugin; Jin, Yiung Seok; Bae, Jieun et al.
2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE Vehicular Technology Conference; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning based In-Cabin Radar System for Passenger Monitoring System

AU - Hyun, Eugin

AU - Jin, Yiung Seok

AU - Bae, Jieun

AU - Chi-Ho, Park

PY - 2023

Y1 - 2023

N2 - In this paper, we propose a passenger monitoring scheme using a 60GHz FMCW radar for in-cabin applications. Based on the 2D images using cloud point, we employed a 2D deep learning method. We found an average recognition rate at 96 %, which was one to five occupants in a vehicle.

AB - In this paper, we propose a passenger monitoring scheme using a 60GHz FMCW radar for in-cabin applications. Based on the 2D images using cloud point, we employed a 2D deep learning method. We found an average recognition rate at 96 %, which was one to five occupants in a vehicle.

KW - in-cabin radar

KW - passenger detection

KW - passenger monitoring

KW - radar cloud point

KW - radar machine learning

UR - https://www.scopus.com/pages/publications/85169799227

U2 - 10.1109/VTC2023-Spring57618.2023.10200306

DO - 10.1109/VTC2023-Spring57618.2023.10200306

M3 - Conference contribution

AN - SCOPUS:85169799227

T3 - IEEE Vehicular Technology Conference

BT - 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 97th IEEE Vehicular Technology Conference, VTC 2023-Spring

Y2 - 20 June 2023 through 23 June 2023

ER -

Machine Learning based In-Cabin Radar System for Passenger Monitoring System

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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