Driver Drowsiness Detection Using Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and Driver Distraction Using Head Pose Estimation

Mounika, T.; Phanindra, P.; Charan, N. V. V. N. Sai; Reddy, Y. Kranthi Kumar; Govindu, Swetha

doi:10.1007/978-981-16-5987-4_63

Cited by 12 publications

(4 citation statements)

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“…Another study [10] discusses detecting driver drowsiness based on adaptive thresholds using EAR and MAR. The system detects the face and determines the facial landmarks by which it can compute EAR and MAR to detect driver drowsiness based on an adaptive threshold.…”

Section: Previous Workmentioning

confidence: 99%

Real-time smart driver sleepiness detection by eye aspect ratio using computer vision

Kai Yuen,

Zakaria,

Eg Su

et al. 2024

IJEECS

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The purpose of this study is to determine the optimal eye aspect ratio (EAR) for a prototype capable of using computer vision techniques to detect driver sleepiness based on eyelid size changes. The prototype, developed with Raspberry Pi and OpenCV, provides a real-time evaluation of the driver's level of alertness. The prototype can accurately determine the onset of sleepiness by monitoring and detecting instances of prolonged eyelid closure. Due to the fact that the eye aspect ratios of different individuals vary in size, the system's accuracy may be compromised. For the first experiment, the research focuses on determining the optimal EAR threshold of the proposed prototype using a sample of 20 participants ranging in age from 20 to 30, 31 to 40, and 41 to 50 years old. The study also examines the effects of various environmental conditions, such as dark or nighttime settings and the use of spectacle. The optimal EAR threshold value, as dedicated by the first experiment, is 0.225 after testing 20 participants with and without eyeglasses in low and bright lighting and 7 participants with a 0.225 EAR threshold in dark and bright lighting environments. The result shows 100% precision.

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Section: Previous Workmentioning

confidence: 99%

Real-time smart driver sleepiness detection by eye aspect ratio using computer vision

Kai Yuen,

Zakaria,

Eg Su

et al. 2024

IJEECS

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“…These include blink frequency, maximum duration of closure of the eyes [13], percentage of eyelid closure [18], eye aspect ratio [19], eyelids' curvature [17], yawning frequency [20], MAR [21], mouth opening time [22], head pose [23], head-nodding frequency [4], and head movement analysis [24]. Combinations of these features have been considered as well [20,21,25]. In this section, we provide a detailed explanation of the features that are used in our proposed system.…”

Section: Related Workmentioning

confidence: 99%

“…Another mouth-based feature is the mouth opening ratio [29]. It is also referred to as the MAR [21]. It describes the opening degree of the mouth as an indicator for yawning.…”

Section: Related Workmentioning

confidence: 99%

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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“…Based on the aspect ratio, we can analyze the openness and closeness of the mouth. If MAR ≥ 0.60 [46] it is considered normal behavior. Between these values, the person might be talking, or if MAR exceeds 0.60 but the time duration is less than 3 seconds it is also considered as 'talking'.…”

Section: ) Eye Aspect Ratio (Ear)mentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

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The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.INDEX TERMS ADAS, driver drowsiness, road safety, driver monitoring, IoT, edge computing, mobile application, road accidents, cloud computing.

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Driver Drowsiness Detection Using Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and Driver Distraction Using Head Pose Estimation

Cited by 12 publications

References 0 publications

Real-time smart driver sleepiness detection by eye aspect ratio using computer vision

Real-time smart driver sleepiness detection by eye aspect ratio using computer vision

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

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

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