research-article

Real-Time System for Driver Fatigue Detection by RGB-D Camera

Authors:

Jinhui TangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 2

Article No.: 22, Pages 1 - 17

https://doi.org/10.1145/2629482

Published: 31 March 2015 Publication History

Abstract

Drowsy driving is one of the major causes of fatal traffic accidents. In this article, we propose a real-time system that utilizes RGB-D cameras to automatically detect driver fatigue and generate alerts to drivers. By introducing RGB-D cameras, the depth data can be obtained, which provides extra evidence to benefit the task of head detection and head pose estimation. In this system, two important visual cues (head pose and eye state) for driver fatigue detection are extracted and leveraged simultaneously. We first present a real-time 3D head pose estimation method by leveraging RGB and depth data. Then we introduce a novel method to predict eye states employing the WLBP feature, which is a powerful local image descriptor that is robust to noise and illumination variations. Finally, we integrate the results from both head pose and eye states to generate the overall conclusion. The combination and collaboration of the two types of visual cues can reduce the uncertainties and resolve the ambiguity that a single cue may induce. The experiments were performed using an inside-car environment during the day and night, and theyfully demonstrate the effectiveness and robustness of our system as well as the proposed methods of predicting head pose and eye states.

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Cited By

Sun ZMiao YJeon JKong YPark G(2023)Facial feature fusion convolutional neural network for driver fatigue detectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106981126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106981
Verma RPargal SDas DParbat TKambalapalli SMitra BChakraborty S(2022)Impact of Driving Behavior on Commuter’s Comfort during Cab Rides: Towards a New Perspective of Driver RatingACM Transactions on Intelligent Systems and Technology10.1145/3523063Online publication date: 22-Mar-2022
https://doi.org/10.1145/3523063
Guria MBhowmik B(2022)IoT-Enabled Driver Drowsiness Detection Using Machine Learning2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC56933.2022.10053235(519-524)Online publication date: 25-Nov-2022
https://doi.org/10.1109/PDGC56933.2022.10053235
Show More Cited By

Index Terms

Real-Time System for Driver Fatigue Detection by RGB-D Camera
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Multiple instruction, multiple data

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Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 2

Special Section on Visual Understanding with RGB-D Sensors

May 2015

381 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2753829

Editor:
Huan Liu
Arizona State University

Issue’s Table of Contents

Copyright © 2015 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

New York, NY, United States

Publication History

Published: 31 March 2015

Accepted: 01 March 2014

Revised: 01 December 2013

Received: 01 June 2013

Published in TIST Volume 6, Issue 2

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National Nature Science Foundation of China
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Cited By

Sun ZMiao YJeon JKong YPark G(2023)Facial feature fusion convolutional neural network for driver fatigue detectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106981126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106981
Verma RPargal SDas DParbat TKambalapalli SMitra BChakraborty S(2022)Impact of Driving Behavior on Commuter’s Comfort during Cab Rides: Towards a New Perspective of Driver RatingACM Transactions on Intelligent Systems and Technology10.1145/3523063Online publication date: 22-Mar-2022
https://doi.org/10.1145/3523063
Guria MBhowmik B(2022)IoT-Enabled Driver Drowsiness Detection Using Machine Learning2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC56933.2022.10053235(519-524)Online publication date: 25-Nov-2022
https://doi.org/10.1109/PDGC56933.2022.10053235
Dziuda ŁBaran PZieliński PMurawski KDziwosz MKrej MPiotrowski MStablewski RWojdas AStrus WGasiul HKosobudzki MBortkiewicz A(2021)Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator StudySensors10.3390/s2119644921:19(6449)Online publication date: 27-Sep-2021
https://doi.org/10.3390/s21196449
Chaabene SBouaziz BBoudaya AHökelmann AAmmar AChaari L(2021)Convolutional Neural Network for Drowsiness Detection Using EEG SignalsSensors10.3390/s2105173421:5(1734)Online publication date: 3-Mar-2021
https://doi.org/10.3390/s21051734
El Khatib AOu CKarray F(2020)Driver Inattention Detection in the Context of Next-Generation Autonomous Vehicles Design: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.294087421:11(4483-4496)Online publication date: Nov-2020
https://doi.org/10.1109/TITS.2019.2940874
Kolodziej MTarnowski PSawicki DMajkowski ARak RBala APluta A(2020)Fatigue Detection Caused by Office Work With the Use of EOG SignalIEEE Sensors Journal10.1109/JSEN.2020.301240420:24(15213-15223)Online publication date: 15-Dec-2020
https://doi.org/10.1109/JSEN.2020.3012404
Murugan SSelvaraj JSahayadhas A(2019)Analysis of Different Measures to Detect Driver States: A Review2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)10.1109/ICSCAN.2019.8878844(1-6)Online publication date: Mar-2019
https://doi.org/10.1109/ICSCAN.2019.8878844
Liu FLi XLv TXu F(2019)A Review of Driver Fatigue Detection: Progress and Prospect2019 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE.2019.8662098(1-6)Online publication date: Jan-2019
https://doi.org/10.1109/ICCE.2019.8662098
Ramirez JRodriguez MAndrade ACastro LBeltran JArmenta J(2019)Inferring Drivers’ Visual Focus Attention Through Head-Mounted Inertial SensorsIEEE Access10.1109/ACCESS.2019.29605677(185422-185432)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2960567
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