Drowsiness Detection Using Eye-Blink Pattern and Mean Eye Landmarks’ Distance

Miah, Abdullah Arafat; Ahmad, Mohiuddin; Mim, Khatuna Zannat

doi:10.1007/978-981-13-7564-4_10

Cited by 18 publications

(9 citation statements)

References 14 publications

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“…7 shows Multi-task ConNN model. Depending on the width of the mesh, the filter size was determined as first (12,15,20) and second (6,8,10) in three convolution layers, respectively. Two experiments were conducted to select network structures including convolution layer capacity, nonlinear activation function and pooling method.…”

Section: Resultsmentioning

confidence: 99%

“…3. In recent driver fatigue detection systems, most studies have focused on using limited visual cues [15]. However, human fatigue is a complex mechanism and depends on the dynamic cohesion of various cues [16], which means that outcomes and situations can be improved.…”

Section: Proposed Approachmentioning

confidence: 99%

“…They used Wild (CEW) and ZJU database for classification. Miah et al [15] conducted a fatigue detection study based on the condition of the eye. The position of the eyes and the average distance of them were determined.…”

Section: Introductionmentioning

confidence: 99%

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Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

2020

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Changes and progresses in information technologies have played an important role in the development of intelligent vehicle systems in recent years. Driver fatigue is an important factor in vehicle accidents. For this reason, traffic accidents involving driver fatigue and driver carelessness have been followed by researchers. In this article, a Multi-tasking Convulational Neural Network (ConNN *) model is proposed to detect driver drowsiness/fatigue. Eye and mouth characteristics are utilized for driver's behavior model. Changes to these characteristics are used to monitor driver fatigue. With the proposed Multi-task ConNN model, unlike the studies in the literature, both mouth and eye information are classified into a single model at the same time. Driver fatigue is determined by calculating eyes' closure duration/Percentage of eye closure (PERCLOS) and yawning frequency/frequency of mouth (FOM). In this study, the fatigue degree of the driver is divided into 3 classes. The proposed model achieved 98.81% fatigue detection on YawdDD and NthuDDD dataset. The success of the model is presented comparatively. INDEX TERMS Convolutional neural network, driver fatigue detection, PERCLOS, FOM. * Convulational Neural Network has been abbreviated as ConNN, not CNN or CoNN, CNN has been used as the abbreviation of Celluar Neural Network and CoNN has been used as the abbreviation of Cooperative neural networks in the leterature as a long time. The associate editor coordinating the review of this manuscript and approving it for publication was Zahid Akhtar .

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

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Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

2020

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“…The duration of time of eye closure is considered to regulate the drowsiness condition. The method achieves an accuracy of 93% [17]. In each section, the pixel's worth is calculated according to the pixel fluctuation ratio.…”

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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“…For many years, scientists have focused on detection of stress, drowsiness, etc. [ 30 , 31 , 32 ]. These methods mainly employ video cameras and conventional imaging technologies, relying often on eye blinking frequency or the percentage of closed eyelids.…”

Section: Introductionmentioning

confidence: 99%

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

Šidiková

Martínek

Kawala-Sterniuk

et al. 2020

Sensors

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This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

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Drowsiness Detection Using Eye-Blink Pattern and Mean Eye Landmarks’ Distance

Cited by 18 publications

References 14 publications

Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

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

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

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