Open AccessDrowsiness Detection for Motorized Vehicles Using Machine Learning
Author(s) -
Twinkle P George
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38232
Subject(s) - alarm , computer science , artificial intelligence , real time computing , computer vision , simulation , engineering , aerospace engineering
Driver drowsiness is one of the major causes for most of the accidents in the world. Detecting the driver's eye tiredness is the easiest way for measuring the drowsiness of the driver. The advent of high-speed motorized vehicles drowsy driving accidents has claimed the lives of millions of people across the globe. To avoid such accidents, proposes a Machine Learning based system drowsiness system for motorized vehicles with alarm and Web Push Notifications to notify the driver before any accident occurs. The driver's face is captured by a real-time camera system, and the eye borders are detected by a pre-trained machine learning model from the real-time video stream. Then each eye is represented by 6 – coordinates (x, y) starting from the left corner of the eye and then working clockwise around the eye. The EAR (Ear Aspect Ratio) is calculated across 20 consecutive frames, and if it falls below a certain threshold, it sounds an alarm and sends the details of the nearest coffee shop to your mobile device via a Web Push Notification. When the alarm is activated, it also displays a list of nearby coffee shops to help the driver stay awake. Keywords: Machine Learning, SVM, MOR, EAR