Driver Distraction Behavior Detection Method Based on Deep Learning

With the rapid development of road traffic in China, driver safety accidents caused by road traffic accidents are increasing year by year. According to statistics of relevant departments, 20%-30% of traffic safety accidents are caused by distracted behaviors of drivers. For this reason, this paper p...

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Published in:	IOP conference series. Materials Science and Engineering Vol. 782; no. 2; pp. 22012 - 22019
Main Authors:	Mao, Peng, Zhang, Kunlun, Liang, Da
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01.03.2020
Subjects:	Algorithms Behavior Deep learning Driver behavior Face recognition Machine learning Object recognition Telematics Telephone calls Traffic accidents Traffic accidents & safety Traffic safety Vehicle safety
ISSN:	1757-8981, 1757-899X
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Abstract	With the rapid development of road traffic in China, driver safety accidents caused by road traffic accidents are increasing year by year. According to statistics of relevant departments, 20%-30% of traffic safety accidents are caused by distracted behaviors of drivers. For this reason, this paper proposes a driver distraction behavior detection method based on deep learning, which uses PCN and DSST algorithms for face detection, location and dynamic face tracking. Finally, YOLOV3 object detection algorithm is used to identify distracting behaviors such as smoking and making phone calls around a person's face. The method can detect distracted behaviors in the driving process in real time and has high detection accuracy.
AbstractList	With the rapid development of road traffic in China, driver safety accidents caused by road traffic accidents are increasing year by year. According to statistics of relevant departments, 20%-30% of traffic safety accidents are caused by distracted behaviors of drivers. For this reason, this paper proposes a driver distraction behavior detection method based on deep learning, which uses PCN and DSST algorithms for face detection, location and dynamic face tracking. Finally, YOLOV3 object detection algorithm is used to identify distracting behaviors such as smoking and making phone calls around a person’s face. The method can detect distracted behaviors in the driving process in real time and has high detection accuracy.
Author	Mao, Peng Zhang, Kunlun Liang, Da
Author_xml	– sequence: 1 givenname: Peng surname: Mao fullname: Mao, Peng organization: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education. School of Electrical Engineering – sequence: 2 givenname: Kunlun surname: Zhang fullname: Zhang, Kunlun email: zhangkunlun@home.swjtu.edu.cn organization: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education. School of Electrical Engineering – sequence: 3 givenname: Da surname: Liang fullname: Liang, Da organization: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education. School of Electrical Engineering
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Cites_doi	10.1016/j.trc.2010.12.002 10.1109/CVPR.2018.00244 10.1109/TITS.2015.2462084 10.1109/JSEN.2007.895971
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Copyright	Published under licence by IOP Publishing Ltd 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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References_xml	– start-page: 122 year: 2000 ident: MSE_782_2_022012bib11 article-title: Real-Time Stereo Tracking for Head Pose and Gaze Estimation – volume: 19 start-page: 673 year: 2011 ident: MSE_782_2_022012bib7 article-title: Automatic system for detecting driver use or mobile phones publication-title: Transportation Research Part C:Emerging Technologies doi: 10.1016/j.trc.2010.12.002 – year: 2018 ident: MSE_782_2_022012bib13 article-title: Yolov3: An incremental improvement – year: 2018 ident: MSE_782_2_022012bib10 article-title: Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks doi: 10.1109/CVPR.2018.00244 – volume: 4 start-page: 5 year: 2015 ident: MSE_782_2_022012bib14 article-title: You only look once: Unified, real-time object detection – start-page: 97 year: 2011 ident: MSE_782_2_022012bib8 article-title: Detecting Driver Phone Use Leveraging Car Speakers – ident: MSE_782_2_022012bib6 article-title: Method and device for automatically obtaining evidence of mobile phone call behavior when driver is driving – volume: 16 start-page: 1 year: 2015 ident: MSE_782_2_022012bib2 article-title: Driver behavior analysis for safe driving: a survey [J] publication-title: IEEE Transactions on Intelligent Transportation on Systems doi: 10.1109/TITS.2015.2462084 – volume: 36 start-page: 298 year: 1994 ident: MSE_782_2_022012bib5 article-title: Human Factors:The Journal of the Human Factors and Ergonomiccs Society – start-page: 1097 year: 2012 ident: MSE_782_2_022012bib9 article-title: Imagenet classification with deep convolutional neural networks – volume: 32 ident: MSE_782_2_022012bib4 article-title: Modeling traffic accident occurrence and involvement publication-title: Accident Analysis & Prevention – year: 2006 ident: MSE_782_2_022012bib1 – year: 2010 ident: MSE_782_2_022012bib12 – year: 2007 ident: MSE_782_2_022012bib3 article-title: Gamma-Free Smoke and Particle Detector Using Tritiated Foils doi: 10.1109/JSEN.2007.895971
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SubjectTerms	Algorithms Behavior Deep learning Driver behavior Face recognition Machine learning Object recognition Telematics Telephone calls Traffic accidents Traffic accidents & safety Traffic safety Vehicle safety
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Title	Driver Distraction Behavior Detection Method Based on Deep Learning
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