Drowsiness Detection and Alert Android App Using OpenCV

Abstract

Drowsiness while driving is a significant cause of road accidents worldwide, with many such accidents resulting in fatalities or severe injuries. In recent years, researchers have been investigating the use of computer vision techniques to detect drowsiness in drivers and alert them to take appropriate action.

The proposed app uses OpenCV, a popular computer vision library, to perform facial landmark detection and eye tracking to monitor the user's level of drowsiness. The app employs a machine learning algorithm trained on a data-set of labeled images to recognize patterns that indicate drowsiness. The facial landmark detection process is carried out by identifying specific points on the  users face, like the corners of the eyes, the nose, and the mouth. These points are then used to compute the eye aspect ratio (EAR), which is a way of  measuring how open the user's eyes are. When the eyes are closed, the EAR value decreases, indicating drowsiness. The app uses this to detect drowsiness and alert the user using an alarm. The proposed drowsiness detection and alert app using OpenCV is an effective and potentially life-saving tool for drivers. It combines computer vision techniques, machine learning algorithms, and user-friendly interfaces to detect drowsiness accurately and alert drivers to take appropriate action.  The app can help reduce the incidence of vehicular collisions resulting from operator fatigue, making the roads safer for everyone.