Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks

A. K. Sriranga; Q. Lu; S. Birrell

doi:10.1109/SSCI51031.2022.10022146

Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks

A. K. Sriranga, Q. Lu, S. Birrell

Centre for Future Transport and Cities

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

2 Citations (Scopus)

151 Downloads (Pure)

Abstract

Vehicle Occupant Detection has gathered attention with the advancement of Connected Automated Vehicles (CAVs) since it enhances vehicular safety features and contributes to Vehicle-to-Everything (V2X) communication features. In this paper, a novel Frequency Modulated Continuous Wave (FMCW) radar-based occupancy detection utilizing Convolutional Neural Networks (CNN) is introduced. The proposed methodology tackles disadvantages posed by visual and sensor-based methods when privacy, computational complexity, line-of-sight requirements, and robustness are concerned. The system uses time-domain raw radar data signals to form visual heatmaps based on signal intensity variation caused by presence of a target. The heatmaps developed for each data frame acts as an input to the neural network. Visually generated signal based heatmaps differentiate three classes of vehicle occupancy: vacant, driver seat and rear passenger occupancy. The adapted CNN architecture is an implementation of transfer learning where a version of the VGG-16 pretrained model consisting of 16 convolutional layers is used. A validation accuracy of 96.88% is achieved with a dataset containing 1000 heatmap images for each class. The results conclude that radar generated time domain heatmaps efficiently detect vehicle occupancy employing transfer learning even with smaller datasets.

Original language	English
Title of host publication	2022 IEEE Symposium Series on Computational Intelligence (SSCI)
Editors	Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Publisher	IEEE
Pages	55-60
Number of pages	6
ISBN (Electronic)	978-1-6654-8768-9
ISBN (Print)	978-1-6654-8769-6
DOIs	https://doi.org/10.1109/SSCI51031.2022.10022146
Publication status	Published - 30 Jan 2023
Event	2022 IEEE Symposium Series on Computational Intelligence (SSCI) - , Singapore Duration: 4 Dec 2022 → 7 Dec 2022

Publication series

Name	2022 IEEE Symposium Series on Computational Intelligence (SSCI)
Publisher	IEEE

Conference

Conference	2022 IEEE Symposium Series on Computational Intelligence (SSCI)
Country/Territory	Singapore
Period	4/12/22 → 7/12/22

Bibliographical note

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

FMCW
CNN
Radar
Transfer Learning
Classification
Vehicle safety
Vehicle occupancy

Access to Document

10.1109/SSCI51031.2022.10022146

Post-PrintAccepted author manuscript, 918 KB

Cite this

Sriranga, A. K., Lu, Q., & Birrell, S. (2023). Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks. In H. Ishibuchi, C.-K. Kwoh, A.-H. Tan, D. Srinivasan, C. Miao, A. Trivedi, & K. Crockett (Eds.), 2022 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 55-60). (2022 IEEE Symposium Series on Computational Intelligence (SSCI)). IEEE. https://doi.org/10.1109/SSCI51031.2022.10022146

Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks. / Sriranga, A. K.; Lu, Q.; Birrell, S.
2022 IEEE Symposium Series on Computational Intelligence (SSCI). ed. / Hisao Ishibuchi; Chee-Keong Kwoh; Ah-Hwee Tan; Dipti Srinivasan; Chunyan Miao; Anupam Trivedi; Keeley Crockett. IEEE, 2023. p. 55-60 (2022 IEEE Symposium Series on Computational Intelligence (SSCI)).

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

Sriranga, AK , Lu, Q & Birrell, S 2023, Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks. in H Ishibuchi, C-K Kwoh, A-H Tan, D Srinivasan, C Miao, A Trivedi & K Crockett (eds), 2022 IEEE Symposium Series on Computational Intelligence (SSCI). 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 55-60, 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, 4/12/22. https://doi.org/10.1109/SSCI51031.2022.10022146

Sriranga AK , Lu Q , Birrell S. Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks. In Ishibuchi H, Kwoh CK, Tan AH, Srinivasan D, Miao C, Trivedi A, Crockett K, editors, 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2023. p. 55-60. (2022 IEEE Symposium Series on Computational Intelligence (SSCI)). doi: 10.1109/SSCI51031.2022.10022146

Sriranga, A. K. ; Lu, Q. ; Birrell, S. / Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks. 2022 IEEE Symposium Series on Computational Intelligence (SSCI). editor / Hisao Ishibuchi ; Chee-Keong Kwoh ; Ah-Hwee Tan ; Dipti Srinivasan ; Chunyan Miao ; Anupam Trivedi ; Keeley Crockett. IEEE, 2023. pp. 55-60 (2022 IEEE Symposium Series on Computational Intelligence (SSCI)).

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N2 - Vehicle Occupant Detection has gathered attention with the advancement of Connected Automated Vehicles (CAVs) since it enhances vehicular safety features and contributes to Vehicle-to-Everything (V2X) communication features. In this paper, a novel Frequency Modulated Continuous Wave (FMCW) radar-based occupancy detection utilizing Convolutional Neural Networks (CNN) is introduced. The proposed methodology tackles disadvantages posed by visual and sensor-based methods when privacy, computational complexity, line-of-sight requirements, and robustness are concerned. The system uses time-domain raw radar data signals to form visual heatmaps based on signal intensity variation caused by presence of a target. The heatmaps developed for each data frame acts as an input to the neural network. Visually generated signal based heatmaps differentiate three classes of vehicle occupancy: vacant, driver seat and rear passenger occupancy. The adapted CNN architecture is an implementation of transfer learning where a version of the VGG-16 pretrained model consisting of 16 convolutional layers is used. A validation accuracy of 96.88% is achieved with a dataset containing 1000 heatmap images for each class. The results conclude that radar generated time domain heatmaps efficiently detect vehicle occupancy employing transfer learning even with smaller datasets.

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ER -

Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this