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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Vydáno v:IOP conference series. Materials Science and Engineering Ročník 782; číslo 2; s. 22012 - 22019
Hlavní autoři: Mao, Peng, Zhang, Kunlun, Liang, Da
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.03.2020
Témata:
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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Popis
Shrnutí: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.
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ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/782/2/022012