Driver Fatigue Detection Based on Eye State Recognition

2017 ◽  
Author(s):  
Fang Zhang ◽  
Jingjing Su ◽  
Lei Geng ◽  
Zhitao Xiao
Keyword(s):  
Driver Fatigue ◽  
State Recognition ◽  
Fatigue Detection

A Practical Eye State Recognition Based Driver Fatigue Detection Method

2009 ◽  
Author(s):  
Huan Wang ◽  
Yong Cheng ◽  
Qiong Wang ◽  
Mingwu Ren ◽  
Chunxia Zhao ◽  
...  
Keyword(s):  
Detection Method ◽  
Driver Fatigue ◽  
State Recognition ◽  
Fatigue Detection

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals Implementation of face and eye detection on DM6437 board using simulink model

2020 ◽  
Vol 9 (2) ◽  
pp. 785-791
Author(s):  
B. Vijayalaxmi ◽  
Kaushik Sekaran ◽  
N. Neelima ◽  
P. Chandana ◽  
Maytham N. Meqdad ◽  
...  
Keyword(s):  
Research Work ◽  
Warning Signal ◽  
Blink Rate ◽  
Driver Fatigue ◽  
Simulink Model ◽  
Detection Stage ◽  
The Face ◽  
Fatigue Estimation ◽  
Motivating Factor ◽  
Fatigue Detection

Driver Assistance system is significant in drriver drowsiness to avoid on road accidents.  The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.

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