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

Li, Kening; Gong, Yunbo; Ren, Ziliang

doi:10.1109/access.2020.2998363

Cited by 92 publications

(45 citation statements)

References 54 publications

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“…Moreover, the training process is performed by using Hypo-DB as mentioned in sub-section 3.1. To perform comparisons with the other state-of-the-art hypovigilance detection systems, four studies are selected such as Du-RNN [18], Li-CNN [20], Chen-SBL [29], and Choi-LSTM [34]. To evaluate these multimodal based systems using machine-learning or deep learning algorithms, we have used the same techniques as implemented in the corresponding research papers.…”

Section: Resultsmentioning

confidence: 99%

“…Their technique uses a single EEG channel to eliminate false detection. The methodology presented in [20] combines both the EEG (diagnostic techniques and fuzzy logic), and two EOG (blinking detection and drowsiness characterization)-based techniques. It reduces the false alarm rate to 5% while increasing the correct classification rate of drowsiness (awake, drowsy, and very drowsy) levels to 80.6%.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Riquelme¹,

Olivares²,

Mu駉z³

et al. 2022

Computers, Materials &Amp; Continua

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Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points (0 • , 45 • , and 90 • ) of the drivers. To develop a Hypo-Driver system, the physiological signals (electroencephalography (EEG), electrocardiography (ECG), electro-myography (sEMG), and electrooculography (EOG)) and behavioral information (PERCLOS70-80-90%, mouth aspect ratio (MAR), eye aspect ratio (EAR), blinking frequency (BF), head-titled ratio (HT-R)) are collected and pre-processed, then followed by feature selection and fusion techniques. The driver behaviors are classified into five stages such as normal, fatigue, visual inattention, cognitive inattention, and drowsy. This improved hypo-Driver system utilized trained behavioral features by a convolutional neural network (CNNs), recurrent neural network and long short-term memory (RNN-LSTM) model is used to extract physiological features. After fusion of these features, the Hypo-Driver system is classified hypo-V into five stages based on trained layers and dropout-layer in the deep-residual neural network (DRNN) model. To test the performance of a hypo-Driver system, data from 20 drivers are acquired. The results of Hypo-Driver compared to state-of-theart methods are presented. Compared to the state-of-the-art Hypo-V system, on average, the Hypo-Driver system achieved a detection accuracy (AC) of 96.5%. The obtained results indicate that the Hypo-Driver system based on multimodal and multiview features outperforms other state-of-the-art driver Hypo-V systems by handling many anomalies.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Riquelme¹,

Olivares²,

Mu駉z³

et al. 2022

Computers, Materials &Amp; Continua

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“…The driver’s eye closure time, the number of blinks, and the number of yawns were calculated to evaluate the fatigue state. As a result, the fatigue state could be detected with 95.10% accuracy [ 69 ]. Képešiová et al obtained grayscale facial image data of 20 collaborators and trained them using the convolutional neural network (CNN).…”

Section: Drowsiness Detection and Estimation Based On Graphic Information Of A Drivermentioning

confidence: 99%

Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Arakawa

2021

Sensors

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Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and driver’s graphic information. This review summarizes the research and development trends of drowsiness detection systems based on various methods. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. In the case of autonomous driving levels 4 and 5, where the driver is not the primary driving agent, the technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination.

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“…At last, the verification model for the driver was built along with the fatigue valuation model. Using virtual applications, the method was capable of detecting the fatigue state with an accuracy of 95.10% [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Vishesh¹,

Raghavendra²,

Jankatti³

et al. 2021

IJEECS

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<span><span>Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the “Xiaoyang Tan” of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety</span>.</span>

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

Cited by 92 publications

References 54 publications

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

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