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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Veröffentlicht in:IOP conference series. Materials Science and Engineering Jg. 782; H. 2; S. 22012 - 22019
Hauptverfasser: Mao, Peng, Zhang, Kunlun, Liang, Da
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.03.2020
Schlagworte:
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
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  organization: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education. School of Electrical Engineering
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  givenname: Kunlun
  surname: Zhang
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  organization: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education. School of Electrical Engineering
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  fullname: Liang, Da
  organization: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education. School of Electrical Engineering
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crossref_primary_10_3390_s22051858
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crossref_primary_10_1109_TITS_2021_3126231
Cites_doi 10.1016/j.trc.2010.12.002
10.1109/CVPR.2018.00244
10.1109/TITS.2015.2462084
10.1109/JSEN.2007.895971
ContentType Journal Article
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Snippet With the rapid development of road traffic in China, driver safety accidents caused by road traffic accidents are increasing year by year. According to...
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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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