Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye

Herrera-Granda, Erick P.; Caraguay-Procel, Jorge A.; Granda-Gudiño, Pedro D.; Herrera-Granda, Israel David; Lorente-Leyva, Leandro L.; Peluffo-Ordóñez, Diego H.; Revelo-Fuelagán, Javier

doi:10.1007/978-3-030-14799-0_54

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…According to the obtained results, it is observed that CNN can give better performance when the processing layers are increased. In [16], an implementation of a real-time system of drowsiness detection was presented for preventing the driver from falling asleep while driving using a sound alert. In this system, the region of the driver's eyes in each video frame is found based on a cascade classifier using the AdaBoost training algorithm in the Viola-Jones method.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of driver's drowsiness assistance model based on eye movements detection

Waleed

Abbas

Hasan

2020

EEJET

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Annually, lots of persons are carrying lifelong disabilities or losing their lives owing to fatal accidents on the road. In addition to mechanical failures and people errors, driver's drowsiness represents one of the fundamental reasons for fatal accidents on the road. When drivers feel drowsy, lots of physiological and behavioral symptoms appear such as changes in the waves of the human brain, changes in facial expressions, variations in eye activity, decreasing head movements, etc. Therefore, there is a significant necessity to provide developed models of driver's drowsiness detection that exploit these symptoms for reducing accidents by warning drivers of drowsiness and fatigue. This paper concentrates on proposing a driver's drowsiness assistance model to monitor and alarm drivers by utilizing a behavioral-based method (eye movements detection method). In the proposed method of detecting eye movements (closed/opened), the Advanced Local Binary Pattern (Advanced LBP) is used, in which the descriptors are utilized to represent eye images to extract the tissue features of different persons in the driving car to see if the driver is in a drowsy state or not and this occurs after recording the driver's video and detecting the eyes of the driver. To extract the features in this way, the image of the eyes is divided into small regions through the Advanced LBP and sequenced into a single feature vector, where this method is used to determine the similarity features in the training group and to classify the eye image. The Naive Bayes classifier (NB) and Support Vector Machine (SVM) are utilized for giving good accuracy. The results indicate that the system has a high accuracy rate compared with the other existing methods, where the accuracy rate of NB and SVM using an eye detection dataset with training 90 % and testing 10% is 96 % and 97 %, respectively

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of driver's drowsiness assistance model based on eye movements detection

Waleed

Abbas

Hasan

2020

EEJET

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“…Driver fatigue increases the need to develop a monitoring system that analyses driver status and provides different alert sounds based on his facial features. Most researchers concentrated on detecting drowsy driving through analyzing the eyes' pupil's parameters [5][6][7]. Through these researches, driver drowsiness investigations are based on capturing the driver's video and detecting the driver's face using some technique.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic approach for detecting drivers’ drowsiness based on image processing and Video streaming

Yousif¹

2021

Preprint

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<p>The driver's face is detected and extracted extra features of the eyes and mouth areas. Start the analysis process to determine the status of these parameters. Second, the Kalman filter method is used to track and manipulate the difference in the size and orientation of the captured features. The technique checks all image states such as brightness, shadows, and clarity. Third, the blur control system provides different alert sounds based on the information tracked from the face, eyes, and mouth. The proposed method uses real-time data recorded by a webcam tool in the MatlabR2016a environment. The data sample contains videos of different users of different races, whether they wear glasses, gender, and various lighting backgrounds. The proposed system achieved an accuracy of up to 94.5% in the detection driver status.</p>

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On the Role of Thermal Imaging in Automotive Applications: A Critical Review

et al. 2023

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Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye

Cited by 6 publications

References 17 publications

Implementation of driver's drowsiness assistance model based on eye movements detection

Implementation of driver's drowsiness assistance model based on eye movements detection

Fuzzy logic approach for detecting drivers’ drowsiness based on image processing and Video streaming

On the Role of Thermal Imaging in Automotive Applications: A Critical Review

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