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.

In this paper, we propose a hybrid real-time DDD system based on the Eyes Closure Ratio and Mouth Opening Ratio using simple camera and deep learning techniques. This system seeks to...

Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The...

vision and machine learning techniques to construct an enhanced system for real-time driver sleepiness detection. By examining the driver's facial expressions—particularly eye closure and yawning—as recorded by an in-car camera, the system attempts to detect indications of fatigue.

Accordingly, Driver's Drowsiness Detection by utilizing a webcam is being acquainted with limit and decrease the quantity of mishaps including vehicles, Lorries, and trucks.

Our proposed system utilizes facial recognition and image processing techniques to analyze facial images captured by an onboard camera, identifying signs of distraction or drowsiness in real-time.

The present study proposes a low-cost, real-time system for monitoring driver drowsiness based on visual behaviour and machine learning. The system computes visual behaviour features such as eye aspect ratio, mouth opening ratio, and nose length ratio from a streaming video captured by a webcam. An adaptive

To detect drowsiness in drivers, the study developed a prototype SAAC system (Advanced Driver Assistance Systems) through a non-invasive method, which captures the physical behavior of the...

By using cameras and real-time video recording systems, modern technology can help us prevent serious traffic accidents by alerting drivers who are feeling drowsy. The system would use Python, OpenCV, and Keras which will warn the driver when he gets tired to prevent these incidents.

(EAR), Eye Closure Ratio (ECR), Mouth Aspect Ratio (MAR) and Mouth Closure Ratio (MCR) to detect driver’s drowsiness based on adaptive thresholding. In case the observed threshold is met then the brakes of the vehicle are applied and the driver is woken up.