Real-Time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing

Yan, Jun-Juh; Kuo, Hang-Hong; Lin, Ying‐Fan; Liao, Teh‐Lu

doi:10.1109/is3c.2016.72

Cited by 54 publications

(18 citation statements)

References 5 publications

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“…For instance, Hiesh and Tai developed an infrared light-based digital signal processing (DSP) embedded system to capture driver's face and detect driving fatigue by identifying the opening and closing of eyes [7]. In another study, a calculation method named as the improved percentage of eyelid closure over the pupil overtime (PERCLOS) was employed as a standard criteria to judge whether the driver was tired or not [8]. Although this method is convenient in fatigue detection, it is vulnerable to environmental illumination such as the brightness, resulting in poor detection performance [9].…”

Section: Introductionmentioning

confidence: 99%

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Chen

et al. 2019

Computational Intelligence and Neuroscience

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The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

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Section: Introductionmentioning

confidence: 99%

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Chen

et al. 2019

Computational Intelligence and Neuroscience

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“…Toyota Motors adopted cameras to measure the distance between the upper and lower eyelids of the driver to determine whether the driver is falling into a sleepy state [3]. Percentage of eyelid closure over time is relatively a reliable method to determine alertness level [4]. But strong light from the vehicles running on the opposite lane may cause false determination because the method needs an ideal light condition.…”

Section: A the Mechanism Of Cyclic Fatigue And Monitoring Methodsmentioning

confidence: 99%

Monitoring Driving Psychological Fatigue Through Unconstrained Heartbeat Signal Extraction by Using Pressure Sensor Array

et al. 2020

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The feasibility of monitoring driving psychological fatigue through unconstrained heartbeat extraction by using pressure sensors array was investigated. The pressure signals were obtained by close contact between subject's back and backrest of car seat. Correlation coefficients of electrocardiogram signals and pressure signals were used to select the optimal monitoring point due to the unavoidable adjustment of sitting posture during long times of driving. Lifting wavelet transform was applied for de-noising. J-J interval signals, which represent heartbeat signals, were extracted. The correlation dimension of heartbeat signals, reflecting the complicated degree and the chaotic level of a nonlinear dynamic system, was used as the indicator to determine the psychological fatigue. Its validity was verified by the consistency of correlation dimension with R-R interval (represents cardiac cycle). Finally, questionnaires during driving were administered. The effectiveness of this method to judge driver psychological fatigue was confirmed. This finding indicates that driving psychological fatigue can be monitored through unconstrained heartbeat signal extraction by using the pressure sensor array.

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“…It has been widely accepted and adopted by many researchers as an effective indicator of fatigue driving. PERCLOS [57] is a physical quantity that measures the state of human fatigue (drowsiness), which is defined as the time taken by the eyes to be closed per unit time. The U.S. Federal Highway Administration and the National Highway Traffic Safety Administration simulated driving in a laboratory, which has verified the effectiveness of PERCLOS in characterizing driver fatigue.…”

Section: E Driver Fatigue Assessment Model 1) Fatigue Judgment Basedmentioning

confidence: 99%

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

Gong

Ren

2020

IEEE Access

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Researches on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms do not analyze driving state from driver characteristics. It results in some inaccuracy. The paper proposes a fatigue driving detection algorithm based on facial multifeature fusion combining driver characteristics. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, on the basis of the Dlib toolkit, we introduce the Eye Feature Vector(EFV) and Mouth Feature Vector(MFV), which are the evaluation parameters of the driver's eye state and mouth state, respectively. Then, the driver identity information library is constructed by offline training, including driver eye state classifier library, driver mouth state classifier library, and driver biometric library. Finally, we construct the driver identity verification model and the driver fatigue assessment model by online assessment. After passing the identity verification, calculate the driver's closed eyes time, blink frequency and yawn frequency to evaluate the driver's fatigue state. In simulated driving applications, our algorithm detects the fatigue state at a speed of over 20fps with an accuracy of 95.10%.

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Real-Time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing

Cited by 54 publications

References 5 publications

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Monitoring Driving Psychological Fatigue Through Unconstrained Heartbeat Signal Extraction by Using Pressure Sensor Array

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

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