Driver Fatigue Detection Based On Facial Feature Analysis

2021 ◽  
pp. 2150034
Author(s):  
Yimin Zhang ◽  
Xianwei Han ◽  
Wei Gao ◽  
Yunliang Hu
Keyword(s):  
Traffic Accidents ◽  
Detection System ◽  
Recognition Rate ◽  
Ccd Camera ◽  
Facial Feature ◽  
The State ◽  
Driver Fatigue ◽  
Eyelid Closure ◽  
Fatigue Driving ◽  
Fatigue Detection

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

scholarly journals Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation

2021 ◽  
Vol 11 (19) ◽  
pp. 9195
Author(s):  
Mu Ye ◽  
Weiwei Zhang ◽  
Pengcheng Cao ◽  
Kangan Liu
Keyword(s):  
Pose Estimation ◽  
Classification Accuracy ◽  
Detection System ◽  
Mouth Opening ◽  
The State ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Driver Fatigue ◽  
Attention Network ◽  
Fatigue Detection

Driver fatigue is the culprit of most traffic accidents. Visual technology can intuitively judge whether the driver is in the state of fatigue. A driver fatigue detection system based on the residual channel attention network (RCAN) and head pose estimation is proposed. In the proposed system, Retinaface is employed for face location and outputs five face landmarks. Then the RCAN is proposed to classify the state of eyes and the mouth. The RCAN includes a channel attention module, which can adaptively extract key feature vectors from the feature map, which significantly improves the classification accuracy of the RCAN. In the self-built dataset, the classification accuracy of the eye state of the RCAN reaches 98.962% and that of the mouth state reaches 98.561%, exceeding other classical convolutional neural networks. The percentage of eyelid closure over the pupil over time (PERCLOS) and the mouth opening degree (POM) are used for fatigue detection based on the state of eyes and the mouth. In addition, this article proposes to use a Perspective-n-Point (PnP) method to estimate the head pose as an essential supplement for driving fatigue detection and proposes over-angle to evaluate whether the head pose is excessively deflected. On the whole, the proposed driver fatigue system integrates 3D head pose estimation and fatigue detection based on deep learning. This system is evaluated by the four datasets and shows success of the proposed method with their high performance.

scholarly journals Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection

2019 ◽  
Vol 11 (5) ◽  
pp. 115 ◽  
Author(s):  
Weihuang Liu ◽  
Jinhao Qian ◽  
Zengwei Yao ◽  
Xintao Jiao ◽  
Jiahui Pan
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Feature Fusion ◽  
Facial Feature ◽  
Detection Algorithm ◽  
Facial Features ◽  
Dynamic Features ◽  
Driver Fatigue ◽  
Stream Network ◽  
Fatigue Detection

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.

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

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.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

scholarly journals Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1298
Author(s):  
Nan Zhao ◽  
Dawei Lu ◽  
Kechen Hou ◽  
Meifei Chen ◽  
Xiangyu Wei ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Covariance Matrices ◽  
Temporal Information ◽  
Pressure Work ◽  
Spatial Features ◽  
Eeg Data ◽  
High Level ◽  
Fatigue Driving ◽  
Fatigue Detection ◽  
Better Than

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.

Real-Time EEG-Based Detection of Fatigue Driving Danger for Accident Prediction

2015 ◽  
Vol 25 (02) ◽  
pp. 1550002 ◽  
Author(s):  
Hong Wang ◽  
Chi Zhang ◽  
Tianwei Shi ◽  
Fuwang Wang ◽  
Shujun Ma
Keyword(s):  
Real Time ◽  
Detection System ◽  
Neural Mechanism ◽  
Processing Capacity ◽  
Driver Fatigue ◽  
Accident Prediction ◽  
Functional Brain Networks ◽  
Long Time ◽  
Danger Detection ◽  
Fatigue Driving

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of driver fatigue was analyzed. The functional brain networks were employed to track the fatigue impact on processing capacity of brain. The results show the overall functional connectivity of the subjects is weakened after long time driving tasks. The regularity is summarized as the fatigue convergence phenomenon. Based on the fatigue convergence phenomenon, we combined both the input and global synchronizations of brain together to calculate the residual amount of the information processing capacity of brain to obtain the dangerous points in real time. Finally, the danger detection system of the driver fatigue based on the neural mechanism was validated using accident EEG. The time distributions of the output danger points of the system have a good agreement with those of the real accident points.

Driver Fatigue Detection System Based on DSP Platform

2016 ◽  
pp. 47-53
Author(s):  
Zibo Li ◽  
Fan Zhang ◽  
Guangmin Sun ◽  
Dequn Zhao ◽  
Kun Zheng
Keyword(s):  
Detection System ◽  
Driver Fatigue ◽  
Fatigue Detection

Driver’s Drowsiness Behaviour Detection by Using PSO/DPSO Algorithm for Urban Road System

2018 ◽  
Vol 7 (3.27) ◽  
pp. 516
Author(s):  
J Sudhakar ◽  
S Srinivasan
Keyword(s):  
Detection System ◽  
State Level ◽  
The State ◽  
Driver Fatigue ◽  
Time System ◽  
Real Time System ◽  
Urban Road ◽  
Vehicle Accidents ◽  
Driver Drowsiness ◽  
The World

In recent years driver fatigue is one of the major causes for vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver drowsiness.  So it is very important to detect the drowsiness of the driver to save life and property. In our system, this aims to develop a prototype of drowsiness detection system. This system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required. Though there are several methods for measuring the drowsiness but this approach is completely non-intrusive which does not affect the driver in any way, hence giving the exact condition of the driver. For detection of drowsiness the each closure value of eye is considered. So when the closure of eye exceeds a certain amount then the driver is identified to be sleepy. The entire system is implemented using PSO, DPSO and FODPSO algorithm and detection of drowsiness behaviour of driver different eye state level.  

Fatigue Detection System Based on Kalman Filtering and Dynamic Template

2013 ◽  
Vol 380-384 ◽  
pp. 3921-3924
Author(s):  
Lan Shi ◽  
Yi Hong Zhang
Keyword(s):  
Detailed Analysis ◽  
Face Detection ◽  
Kalman Filtering ◽  
Detection Rate ◽  
Traffic Accidents ◽  
Detection System ◽  
Eye Detection ◽  
New Approach ◽  
Dynamic Template ◽  
Fatigue Detection

In order to reduce the incidence of traffic accidents, it did detailed analysis and further research on fatigue detection system.this system include face detection, eye detection, eye tracking. During the eye detection, it proposed a new approach based on Kalman filtering and dynamic template. And then it did experiments on the detection rate, the PERCLOSs numerical value and the speed. Experiment results show that the detection results can meet the demand of practice. It turns out that this system can meet the demand of basic practice, has an extensive application field.

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