RF Sensing for Smoking Detection at Oil Fields

Hira Hameed; Naila Azam; Muhammad Usman; Hasan Abbas; Muhammad Ali Imran; Qammer H. Abbasi

doi:10.1109/AP-S/USNC-URSI47032.2022.9887288

RF Sensing for Smoking Detection at Oil Fields

Hira Hameed
, Naila Azam
, Muhammad Usman
, Hasan Abbas
, Muhammad Ali Imran
, Qammer H. Abbasi

University of Glasgow

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

5 Scopus citations

Abstract

In this paper, an ultra-wideband (UWB) Radar sensor is used to detect human gestures while smoking or vaping in potentially dangerous areas such as an oil field or a gas station. Existing smoking detection systems are primarily camera-based, which has a number of drawbacks, including poor illumination, training issues with longer video sequence data, and major privacy concerns. The data collected from a UWB Radar is represented in the form of spectrograms. Three classes are considered, namely cigarette, vape and when the subject is not smoking. InceptionV3, VGG19, and VGG16 deep learning algorithms are used to extract spatiotemporal information from the Spectrogram. Finally, by classifying the Spectrograms into the considered gestures, the smoking and/or vaping is accurately identified. The simulation results show that InceptionV3 can achieve a maximum classification accuracy of 90.00%.

Original language	English
Title of host publication	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	944-945
Number of pages	2
ISBN (Electronic)	9781665496582
DOIs	https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887288
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, United States Duration: 10 Jul 2022 → 15 Jul 2022

Publication series

Name	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

Conference

Conference	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Country/Territory	United States
City	Denver
Period	10/07/22 → 15/07/22

Keywords

RF sensing
Smoking detection
UWB Radar
deep learning

Access to Document

10.1109/AP-S/USNC-URSI47032.2022.9887288

Cite this

Hameed, H., Azam, N., Usman, M., Abbas, H., Imran, M. A., & Abbasi, Q. H. (2022). RF Sensing for Smoking Detection at Oil Fields. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings (pp. 944-945). (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887288

Hameed, Hira ; Azam, Naila ; Usman, Muhammad et al. / RF Sensing for Smoking Detection at Oil Fields. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 944-945 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

@inproceedings{c63e8116655a482d974e69baeec4fdbd,

title = "RF Sensing for Smoking Detection at Oil Fields",

abstract = "In this paper, an ultra-wideband (UWB) Radar sensor is used to detect human gestures while smoking or vaping in potentially dangerous areas such as an oil field or a gas station. Existing smoking detection systems are primarily camera-based, which has a number of drawbacks, including poor illumination, training issues with longer video sequence data, and major privacy concerns. The data collected from a UWB Radar is represented in the form of spectrograms. Three classes are considered, namely cigarette, vape and when the subject is not smoking. InceptionV3, VGG19, and VGG16 deep learning algorithms are used to extract spatiotemporal information from the Spectrogram. Finally, by classifying the Spectrograms into the considered gestures, the smoking and/or vaping is accurately identified. The simulation results show that InceptionV3 can achieve a maximum classification accuracy of 90.00\%.",

keywords = "RF sensing, Smoking detection, UWB Radar, deep learning",

author = "Hira Hameed and Naila Azam and Muhammad Usman and Hasan Abbas and Imran, \{Muhammad Ali\} and Abbasi, \{Qammer H.\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 ; Conference date: 10-07-2022 Through 15-07-2022",

year = "2022",

doi = "10.1109/AP-S/USNC-URSI47032.2022.9887288",

language = "English",

series = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",

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

pages = "944--945",

booktitle = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",

address = "United States",

}

Hameed, H, Azam, N, Usman, M, Abbas, H, Imran, MA & Abbasi, QH 2022, RF Sensing for Smoking Detection at Oil Fields. in 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 944-945, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022, Denver, United States, 10/07/22. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887288

RF Sensing for Smoking Detection at Oil Fields. / Hameed, Hira; Azam, Naila; Usman, Muhammad et al.
2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 944-945 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

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

TY - GEN

T1 - RF Sensing for Smoking Detection at Oil Fields

AU - Hameed, Hira

AU - Azam, Naila

AU - Usman, Muhammad

AU - Abbas, Hasan

AU - Imran, Muhammad Ali

AU - Abbasi, Qammer H.

PY - 2022

Y1 - 2022

N2 - In this paper, an ultra-wideband (UWB) Radar sensor is used to detect human gestures while smoking or vaping in potentially dangerous areas such as an oil field or a gas station. Existing smoking detection systems are primarily camera-based, which has a number of drawbacks, including poor illumination, training issues with longer video sequence data, and major privacy concerns. The data collected from a UWB Radar is represented in the form of spectrograms. Three classes are considered, namely cigarette, vape and when the subject is not smoking. InceptionV3, VGG19, and VGG16 deep learning algorithms are used to extract spatiotemporal information from the Spectrogram. Finally, by classifying the Spectrograms into the considered gestures, the smoking and/or vaping is accurately identified. The simulation results show that InceptionV3 can achieve a maximum classification accuracy of 90.00%.

AB - In this paper, an ultra-wideband (UWB) Radar sensor is used to detect human gestures while smoking or vaping in potentially dangerous areas such as an oil field or a gas station. Existing smoking detection systems are primarily camera-based, which has a number of drawbacks, including poor illumination, training issues with longer video sequence data, and major privacy concerns. The data collected from a UWB Radar is represented in the form of spectrograms. Three classes are considered, namely cigarette, vape and when the subject is not smoking. InceptionV3, VGG19, and VGG16 deep learning algorithms are used to extract spatiotemporal information from the Spectrogram. Finally, by classifying the Spectrograms into the considered gestures, the smoking and/or vaping is accurately identified. The simulation results show that InceptionV3 can achieve a maximum classification accuracy of 90.00%.

KW - RF sensing

KW - Smoking detection

KW - UWB Radar

KW - deep learning

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U2 - 10.1109/AP-S/USNC-URSI47032.2022.9887288

DO - 10.1109/AP-S/USNC-URSI47032.2022.9887288

M3 - Conference contribution

AN - SCOPUS:85139766866

T3 - 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

SP - 944

EP - 945

BT - 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

Y2 - 10 July 2022 through 15 July 2022

ER -

Hameed H, Azam N, Usman M, Abbas H, Imran MA, Abbasi QH. RF Sensing for Smoking Detection at Oil Fields. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 944-945. (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). doi: 10.1109/AP-S/USNC-URSI47032.2022.9887288

RF Sensing for Smoking Detection at Oil Fields

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