A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals

Yura Seo; Miseon Han; Jeongtae Kim

doi:10.1109/SENSORS52175.2022.9967284

A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals

Yura Seo, Miseon Han, Jeongtae Kim

Electronic and Electrical Engineering

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

1 Scopus citations

Abstract

We propose a people counting system using Frequency Modulated Continuous Wave (FMCW) radar signals. The proposed method predicts the number of people using deep learning classification with auto-correlation functions of estimated breathing signals. We also attempted to explain the behavior of the deep learning system using modified gradient-weighted class activation mapping (GradCAM). In the experiments using real radar signals, the proposed method showed improved performance from a conventional method.

Original language	English
Title of host publication	2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665484640
DOIs	https://doi.org/10.1109/SENSORS52175.2022.9967284
State	Published - 2022
Event	2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States Duration: 30 Oct 2022 → 2 Nov 2022

Publication series

Name	Proceedings of IEEE Sensors
Volume	2022-October
ISSN (Print)	1930-0395
ISSN (Electronic)	2168-9229

Conference

Conference	2022 IEEE Sensors Conference, SENSORS 2022
Country/Territory	United States
City	Dallas
Period	30/10/22 → 2/11/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Access to Document

10.1109/SENSORS52175.2022.9967284

Cite this

Seo, Y., Han, M., & Kim, J. (2022). A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals. In 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings (Proceedings of IEEE Sensors; Vol. 2022-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SENSORS52175.2022.9967284

@inproceedings{d78b2556bc154efc94f2d8bca860fa8b,

title = "A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals",

abstract = "We propose a people counting system using Frequency Modulated Continuous Wave (FMCW) radar signals. The proposed method predicts the number of people using deep learning classification with auto-correlation functions of estimated breathing signals. We also attempted to explain the behavior of the deep learning system using modified gradient-weighted class activation mapping (GradCAM). In the experiments using real radar signals, the proposed method showed improved performance from a conventional method.",

author = "Yura Seo and Miseon Han and Jeongtae Kim",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Sensors Conference, SENSORS 2022 ; Conference date: 30-10-2022 Through 02-11-2022",

year = "2022",

doi = "10.1109/SENSORS52175.2022.9967284",

language = "English",

series = "Proceedings of IEEE Sensors",

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

booktitle = "2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings",

}

Seo, Y, Han, M & Kim, J 2022, A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals. in 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings. Proceedings of IEEE Sensors, vol. 2022-October, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE Sensors Conference, SENSORS 2022, Dallas, United States, 30/10/22. https://doi.org/10.1109/SENSORS52175.2022.9967284

A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals. / Seo, Yura; Han, Miseon; Kim, Jeongtae.
2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of IEEE Sensors; Vol. 2022-October).

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

TY - GEN

T1 - A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals

AU - Seo, Yura

AU - Han, Miseon

AU - Kim, Jeongtae

PY - 2022

Y1 - 2022

N2 - We propose a people counting system using Frequency Modulated Continuous Wave (FMCW) radar signals. The proposed method predicts the number of people using deep learning classification with auto-correlation functions of estimated breathing signals. We also attempted to explain the behavior of the deep learning system using modified gradient-weighted class activation mapping (GradCAM). In the experiments using real radar signals, the proposed method showed improved performance from a conventional method.

AB - We propose a people counting system using Frequency Modulated Continuous Wave (FMCW) radar signals. The proposed method predicts the number of people using deep learning classification with auto-correlation functions of estimated breathing signals. We also attempted to explain the behavior of the deep learning system using modified gradient-weighted class activation mapping (GradCAM). In the experiments using real radar signals, the proposed method showed improved performance from a conventional method.

UR - https://www.scopus.com/pages/publications/85144034557

U2 - 10.1109/SENSORS52175.2022.9967284

DO - 10.1109/SENSORS52175.2022.9967284

M3 - Conference contribution

AN - SCOPUS:85144034557

T3 - Proceedings of IEEE Sensors

BT - 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Sensors Conference, SENSORS 2022

Y2 - 30 October 2022 through 2 November 2022

ER -

A 3D CNN based People Counting System Using Auto-Correlation Functions from Frequency Modulated Continuous Wave Radar Signals

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Fingerprint

Cite this