Driver Drowsiness Detection - A Snippet of ADAS Using OpenCV

These days, driving while fatigued is a serious problem. Drivers of buses, trucks, and cars travel great distances both during the day and at night. Accidents and losses on the road are mostly caused by driving, even while fatigued. Establishing a mechanism to track an individual's level of tiredness and its manifestation is crucial. A behavioral system is the one that was created. A method for supervised learning is utilized to identify tiredness. The current method uses a camera to capture the footage, detects the driver's face frontal characteristics (such as their lips and eyes), and applies machine learning algorithms to each frame. The crucial figures determining sleepiness are the eye aspect ratio (EAR) and mouth aspect ratio (MAR), which can be identified with the help of the OpenCV library for facial recognition. This targets the driver's visible facial characteristics. The OpenCV can recognize a person's face with ease, and if the driver exhibits even the slightest hint of sleepiness, the individual is alerted. This contributes to a reduction in traffic accidents, which in turn saves many lives.