Open AccessResearch on Fatigue Driving Monitoring Model and Key Technologies Based on Multi-input Deep Learning
Author(s) -
Jinfeng Liu,
Guang Li,
Jiyan Zhou,
Dunlu Lu,
Bingchu Chen,
Feiyong He
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1648/2/022112
Subject(s) - deep learning , key (lock) , computer science , foundation (evidence) , state (computer science) , safe driving , artificial intelligence , engineering , automotive engineering , computer security , archaeology , algorithm , history
Monitoring and controlling the driver’s fatigue state plays an important role in reducing the traffic accident rate and traffic casualties caused by fatigue driving, so it has high research value and lay a technical foundation with the development of tech. Based on this, this paper first studies the key tech of fatigue driving monitoring based on vision, then analyses the driver’s eye state recognition, and finally studies the driver’s facial dynamic fatigue expression recognition and model establishment based on multi-input deep learning.