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...

Full description

Saved in:
Bibliographic Details
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/782/2/022012