A practical driver fatigue detection algorithm based on eye state

Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.

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Driver Fatigue Detection Algorithm Research Based on the Characteristics of Eyes

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.701-702.30 ◽

2014 ◽

Vol 701-702 ◽

pp. 30-35 ◽

Cited By ~ 2

Author(s):

Qin Wang ◽

Lan Tang ◽

Kun Yang

Keyword(s):

Warning System ◽

Detection Algorithm ◽

Detection Accuracy ◽

Driver Fatigue ◽

Infrared Video ◽

The Face ◽

Eyes Open ◽

Texture Detection ◽

Opening And Closing ◽

Fatigue Detection

In driver fatigue warning system, it is a very effective method for detecting Driver fatigue state through the driver's facial expressions and body movements. The main content of this article is to detect the two basic states of the eyes opening and closing and presents the LBP texture detection operator. Firstly we get the face image sequences using infrared video and extract the eye region using ADABOOST. The SVM is used in classifying feature vector of the eyes open and closed detecting of driver fatigue. A large number of experimental results show that the proposed method has high detection accuracy and timeliness.

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A comparison of edge detection algorithm using for driver fatigue detection system

2010 The 2nd International Conference on Industrial Mechatronics and Automation ◽

10.1109/icindma.2010.5538090 ◽

2010 ◽

Cited By ~ 1

Author(s):

Qingwei Liao ◽

Jingxin Hong ◽

Meiqun Jiang

Keyword(s):

Edge Detection ◽

Detection System ◽

Detection Algorithm ◽

Driver Fatigue ◽

Edge Detection Algorithm ◽

Fatigue Detection

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Eye Statement Recognition for Driver Fatigue Detection Based on Gabor Wavelet and HMM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.128-129.123 ◽

2011 ◽

Vol 128-129 ◽

pp. 123-129

Author(s):

Hai Yan Yang ◽

Xin Hua Jiang ◽

Lei Wang ◽

Yong Hui Zhang

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Good Accuracy ◽

Hidden Markov ◽

Detection Algorithm ◽

Gabor Wavelet ◽

Recognition Method ◽

Driver Fatigue ◽

Gabor Features ◽

Fatigue Detection

Eye statement is one of the most important factors reflecting driver fatigue. A novel eye statement recognition method for driver fatigue detection based on Gabor transformation and Hidden Markov Model is proposed in this paper, in which, the eye detection algorithm is borrowed from Zafer Savas' TrackEye software, and Gabor features, i.e. the eye state features, of the eye are extracted by using Gabor wavelet. After that, by using these features, the classifier is trained by HMM (Hidden Markov Model) to distinguish the eye states including fatigue and alert, then the consecutive five frames are considered to judge whether there exists driver fatigue or not. Simulation results show that the new method has good accuracy and effectiveness.

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Driver fatigue detection algorithm based on eye features

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery ◽

10.1109/fskd.2010.5569306 ◽

2010 ◽

Cited By ~ 3

Author(s):

Jie Tang ◽

Zuhua Fang ◽

Shifeng Hu ◽

Ying Sun

Keyword(s):

Detection Algorithm ◽

Driver Fatigue ◽

Fatigue Detection

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Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN

Computational Intelligence and Neuroscience ◽

10.1155/2020/7251280 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Zuopeng Zhao ◽

Nana Zhou ◽

Lan Zhang ◽

Hualin Yan ◽

Yi Xu ◽

...

Keyword(s):

Mouth Opening ◽

Region Of Interest ◽

Detection Algorithm ◽

Feature Points ◽

Driver Fatigue ◽

Convolutional Network ◽

Eyelid Closure ◽

Two Parameters ◽

Fatigue Driving ◽

Fatigue Detection

With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named EM-CNN, is proposed to detect the states of the eyes and mouth from the ROI images. The percentage of eyelid closure over the pupil over time (PERCLOS) and mouth opening degree (POM) are two parameters used for fatigue detection. Experimental results demonstrate that the proposed EM-CNN can efficiently detect driver fatigue status using driving images. The proposed algorithm EM-CNN outperforms other CNN-based methods, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet50, showing accuracy and sensitivity rates of 93.623% and 93.643%, respectively.

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