Realtime Driver Drowsiness Detection Using Machine Learning

Redhaei, Aneesa Al; Albadawi, Yaman; Mohamed, Safia; Alnoman, Ali

doi:10.1109/aset53988.2022.9734801

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…The most common features used to detect drowsiness in image-based systems are extracted from the eye region. Several researchers proposed the EAR [26][27][28] as a simple metric to detect eye blinking using facial landmarks. It is utilized to estimate the eye openness degree.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features

2023

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Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. This work presents a non-invasive system for real-time driver drowsiness detection using visual features. These features are extracted from videos obtained from a camera installed on the dashboard. The proposed system uses facial landmarks and face mesh detectors to locate the regions of interest where mouth aspect ratio, eye aspect ratio, and head pose features are extracted and fed to three different classifiers: random forest, sequential neural network, and linear support vector machine classifiers. Evaluations of the proposed system over the National Tsing Hua University driver drowsiness detection dataset showed that it can successfully detect and alarm drowsy drivers with an accuracy up to 99%.

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Section: Related Workmentioning

confidence: 99%

“…After splitting the dataset, three classification models were applied: RF, sequential NN [43], and SVM. Then, the parameters of the three models were tuned and optimized by utilizing grid search hyperparameters [28].…”

Section: Classificationmentioning

confidence: 99%

Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features

2023

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“…The technology is adaptable enough to be used by any organisation, and it can identify whether or not drivers entering the facility are wearing seat belts. [4]The objective of this article is to develop a system that employs visual signals to identify driver fatigue in real time. By analysing the aspect ratio of the eye, the system expects to detect indicators of tiredness.…”

Section: Et Al Ds Bhupal Naik In This Papermentioning

confidence: 99%

In Cabin Driver Monitoring and Alerting System For Passenger cars using Machine Learning.

Suryavanshi,

Ladhwe,

Joshi

2023

J. Phys.: Conf. Ser.

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The primary causes of road accidents are driver errors. Such as excessive speeding, driver distraction, and driver sleepiness. Diver sleepiness and distraction can be controlled by implementing advanced technology such as an in-cabin driver monitoring system. This is a technological solution that uses a machine learning algorithm to improve road safety. This paper describes a new real-time driver monitoring and alerting system that solely monitors driver Sleepiness, diver distraction, and seatbelt wearing status in order to prevent road accidents. It employs the YOLOv5 deep learning algorithm to detect various types of distraction and For seatbelt wearing status and computer vision’s cvzone framework is used to detect sleepiness using 3D facial landmarking. In this system, The camera is mounted in the centre of the vehicle’s dashboard for data collection and real-time implementation. The Raspberry Pi 3b module is used for the Realtime implementation. According to the findings of this study, the system is capable of real-time implementation, and the machine learning model used in the study achieves an accuracy of more than 90%.

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“…Facial expression detection can offer more precise detection with minimal effect on the driver [15], [16]. The facial expressions taken are closing the eyes and opening the mouth, which is widely used as a basis for detecting driver fatigue [17], [18]. Facial expressions can be captured using a camera placed in front of the driver.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns

Setyo Utomo,

Rachmawati,

Sthevanie

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The general population is concerned with traffic accidents. Driver fatigue is one of the leading causes of car accidents. Several factors, including nighttime driving, sleep deprivation, alcohol consumption, driving on monotonous roads, and drowsy and fatigue-inducing drugs, can contribute to fatigue. This study proposes a facial appearance-based driver fatigue detection system. This is based on the assumption that facial features can be used to identify driver fatigue. We categorize driver conditions into three groups: normal, talking, and yawning. In this study, we used Adaboost to propose Boosting Local Binary Patterns (LBP) to improve the image features of fatigue drivers in the Support Vector Machine (SVM) model. The experimental results indicate that the system's optimal performance achieves an accuracy value of 93.68%, a recall value of 94%, and a precision value of 94%.

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Realtime Driver Drowsiness Detection Using Machine Learning

Cited by 14 publications

References 18 publications

Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features

Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features

In Cabin Driver Monitoring and Alerting System For Passenger cars using Machine Learning.

Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns

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