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

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.

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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

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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",

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year = "2022",

doi = "10.1109/SENSORS52175.2022.9967284",

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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

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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.

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

Abstract

Publication series

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