Optical correlator based algorithm for driver drowsiness detection

Ouabida, Elhoussaine; Essadike, Abdelaziz; Bouzid, Abdenbi

doi:10.1016/j.ijleo.2019.164102

Cited by 23 publications

(17 citation statements)

References 25 publications

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“…Other issues associated with such systems are the presence of additional features on the face, such as sunglasses, a beard, or a mustache, that may cover the eye or mouth and lead to a system failure. Additional challenges include the random head movement [38,41,52], different skin colors, various lighting conditions [55,130], face's distance from the camera, different face structure based on race, and real-time video analysis that require powerful computing resources [103]. All of that may reduce the accuracy or even lead to false detection.…”

Section: Challengesmentioning

confidence: 99%

“…Optical correlator based DDD algorithm Ouabida et al [41] proposed a fast method for DDD that depends on an optical correlator to detect the eye and then estimates its state using optical correlation with a deformed filter. This method was the first to use a numerical simulation of the optical Vander Lugt correlator [42,43] to detect the eye center automatically.…”

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confidence: 99%

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A Review of Recent Developments in Driver Drowsiness Detection Systems

Albadawi

Takruri

Awad

2022

Sensors

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Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.

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Section: Challengesmentioning

confidence: 99%

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confidence: 99%

A Review of Recent Developments in Driver Drowsiness Detection Systems

Albadawi

Takruri

Awad

2022

Sensors

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“…Various researches were undergone for drowsiness detection to prevent the accidents. The researches undergone previously are as follows: Ouabida et al (2020) developed an Optical Correlator (OC)-based algorithm for driver drowsiness detection. A novel and fast method based on an OC is proposed for eye detection first, followed by eye state estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, these systems are subject to several limitations such as vehicle type, driver experience, geometric characteristics and condition of the road. Since these procedures require a considerable amount of time to analyze user behaviors (Ouabida et al , 2020; Zhang et al , 2020; Tipprasert et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

An ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis

Sharanabasappa

Nandyal²

2021

IJICC

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PurposeIn order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.Design/methodology/approachThe proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.FindingsThe output of these models is classified into non-drowsiness or drowsiness categories.Research limitations/implicationsIn this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.Practical implicationsThis paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.Originality/valueDrowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

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“…When the eyes remain in these states for a long period of time, then, the driver is suffering from unusual behavior. The system of eyes state detection should be capable of reliably detecting and distinguishing these diverse eyes states [10]. Therefore, studies concentrate on the process of detecting the eyes state to specify whether a driver is drowsy.…”

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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Optical correlator based algorithm for driver drowsiness detection

Cited by 23 publications

References 25 publications

A Review of Recent Developments in Driver Drowsiness Detection Systems

A Review of Recent Developments in Driver Drowsiness Detection Systems

An ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis

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

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