A Real-Time Driver Drowsiness Detection System Using MediaPipe and Eye Aspect Ratio
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915623220543488 |
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| author | Sawant, Ashlesha G. Kamble, Shreyash S. Kanade, Raj S. Kanugo, Raunak N. Kapse, Tanishq A. Bhapse, Karan A. |
| author_facet | Sawant, Ashlesha G. Kamble, Shreyash S. Kanade, Raj S. Kanugo, Raunak N. Kapse, Tanishq A. Bhapse, Karan A. |
| contents | One of the major causes of road accidents is driver fatigue that causes thousands of fatalities and injuries every year. This study shows development of a Driver Drowsiness Detection System meant to improve the safety of the road by alerting drivers who are showing signs of being drowsy. The system is based on a standard webcam that tracks the facial features of the driver with the main emphasis on the examination of eye movements that can be conducted with the help of the Eye Aspect Ratio (EAR) method. The Face Mesh by MediaPipe is a lightweight framework that can identify facial landmarks with high accuracy and efficiency, which is considered to be important in real time use. The system detects the moments of long eye shutdowns or a very low rate of blinking which are manifestations of drowsiness and alerts the driver through sound to get her attention back. This system achieves a high-performance and low-cost driver monitoring solution with the help of the computational power of OpenCV to process the image and the MediaPipe to identify faces. Test data experimental analyses indicate that the system is very accurate and responds quicker; this confirms that it can be a component of the current Advanced Driving Assistance System (ADAS). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13618 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Real-Time Driver Drowsiness Detection System Using MediaPipe and Eye Aspect Ratio Sawant, Ashlesha G. Kamble, Shreyash S. Kanade, Raj S. Kanugo, Raunak N. Kapse, Tanishq A. Bhapse, Karan A. Computer Vision and Pattern Recognition One of the major causes of road accidents is driver fatigue that causes thousands of fatalities and injuries every year. This study shows development of a Driver Drowsiness Detection System meant to improve the safety of the road by alerting drivers who are showing signs of being drowsy. The system is based on a standard webcam that tracks the facial features of the driver with the main emphasis on the examination of eye movements that can be conducted with the help of the Eye Aspect Ratio (EAR) method. The Face Mesh by MediaPipe is a lightweight framework that can identify facial landmarks with high accuracy and efficiency, which is considered to be important in real time use. The system detects the moments of long eye shutdowns or a very low rate of blinking which are manifestations of drowsiness and alerts the driver through sound to get her attention back. This system achieves a high-performance and low-cost driver monitoring solution with the help of the computational power of OpenCV to process the image and the MediaPipe to identify faces. Test data experimental analyses indicate that the system is very accurate and responds quicker; this confirms that it can be a component of the current Advanced Driving Assistance System (ADAS). |
| title | A Real-Time Driver Drowsiness Detection System Using MediaPipe and Eye Aspect Ratio |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.13618 |