Open AccessMulti-feature fatigue driving detection based on computer vision
Author(s) -
Juan Huang,
Zihui Lin
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 - 10.1088/1742-6596/1651/1/012188
Subject(s) - adaboost , feature (linguistics) , artificial intelligence , computer science , computer vision , pattern recognition (psychology) , face detection , face (sociological concept) , support vector machine , facial recognition system , social science , sociology , philosophy , linguistics
Fatigue driving is one of the main causes of traffic accidents. This paper proposes a fatigue detection method based on computer vision. The first is the introduction of an optimized algorithm, based on AdaBoost, to detect the face area, and then the ERT algorithm is used to achieve precise localization of the facial landmarks. Finally, a variety of fatigue features of eyes and mouth state associated with driving fatigue are extracted, and after the fusion of all these features, the fatigue driving detection is performed. The experimental results show that multi-feature detection is more accurate than single feature detection.