research-article

Driver's Gaze Zone Estimation Method: A Four-channel Convolutional Neural Network Model

Authors:

Zhe MaAuthors Info & Claims

BDSIC '20: Proceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation

Pages 20 - 24

https://doi.org/10.1145/3440054.3440058

Published: 01 February 2021 Publication History

Abstract

Driver's gaze has become an important indicator to analysis driving state. By estimating the gaze zone of drivers, we can further judge their fatigue state and even predict their driving intention in the next step. In this paper, we propose a four-channel gaze estimation model based on Convolutional Neural Network (CNN), which is used to estimate the gaze zones of the driver. In the proposed method, the images of the right eye, the left eye, the face, and the head are used as the input data of the multi-channel CNN. Then, the features of different channels are fused to estimate the gaze zone. Finally, we compared our method with several existing methods, and the experimental results show that the accuracy of our method is 96%.

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Cited By

Abdelrahman AHempel TKhalifa AAl-Hamadi A(2024)Fine-grained gaze estimation based on the combination of regression and classification lossesApplied Intelligence10.1007/s10489-024-05778-3Online publication date: 3-Sep-2024
https://doi.org/10.1007/s10489-024-05778-3
Khairdoost NBeauchemin SBauer M(2022)An Analytical Model for Estimating Average Driver Attention Based on the Visual Field2022 7th International Conference on Signal and Image Processing (ICSIP)10.1109/ICSIP55141.2022.9886244(586-590)Online publication date: 20-Jul-2022
https://doi.org/10.1109/ICSIP55141.2022.9886244
Çeven SAlbayrak ABayır R(2022)Young Driver Gaze (YDGaze): Dataset for driver gaze analysis2022 International Conference on Artificial Intelligence of Things (ICAIoT)10.1109/ICAIoT57170.2022.10121856(1-6)Online publication date: 29-Dec-2022
https://doi.org/10.1109/ICAIoT57170.2022.10121856
Show More Cited By

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BDSIC '20: Proceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation

December 2020

69 pages

ISBN:9781450388399

DOI:10.1145/3440054

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 February 2021

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the Shandong Provincial Key R&D Program
the National Natural Science Foundation of China

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BDSIC 2020

BDSIC 2020: 2020 2nd International Conference on Big-data Service and Intelligent Computation

December 3 - 5, 2020

Xiamen, China

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Cited By

Abdelrahman AHempel TKhalifa AAl-Hamadi A(2024)Fine-grained gaze estimation based on the combination of regression and classification lossesApplied Intelligence10.1007/s10489-024-05778-3Online publication date: 3-Sep-2024
https://doi.org/10.1007/s10489-024-05778-3
Khairdoost NBeauchemin SBauer M(2022)An Analytical Model for Estimating Average Driver Attention Based on the Visual Field2022 7th International Conference on Signal and Image Processing (ICSIP)10.1109/ICSIP55141.2022.9886244(586-590)Online publication date: 20-Jul-2022
https://doi.org/10.1109/ICSIP55141.2022.9886244
Çeven SAlbayrak ABayır R(2022)Young Driver Gaze (YDGaze): Dataset for driver gaze analysis2022 International Conference on Artificial Intelligence of Things (ICAIoT)10.1109/ICAIoT57170.2022.10121856(1-6)Online publication date: 29-Dec-2022
https://doi.org/10.1109/ICAIoT57170.2022.10121856
Yang YLiu CChang FLu YLiu H(2021)Driver Gaze Zone Estimation via Head Pose Fusion Assisted Supervision and Eye Region Weighted EncodingIEEE Transactions on Consumer Electronics10.1109/TCE.2021.312700667:4(275-284)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1109/TCE.2021.3127006
Zhang YYang XFeng Z(2021)Driving distraction detection based on gaze activityElectronics Letters10.1049/ell2.1228657:22(857-859)Online publication date: 4-Aug-2021
https://doi.org/10.1049/ell2.12286

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