An autonomous car crash prevention system based on behavioral assessment of driver

Abstract

Many road-side accidents occur due to the driver being not in the emotional state of driving. i.e., the driver is fatigued or is not alert. Computer Vision is one of the widely used fields in the world right now. The amount of work being carried out in this field is enormous and very helpful as well. One of such works is detecting human mood at any given time by analyzing the facial expressions of that person. The mood can be of these types. i.e., Alert, Fatigued, Happy, Sad, Drowsy etc. The "OpenCV" open-source Computer Vision library makes it possible to analyze facial expressions. In this thesis, different behavioral assessments are made on a car driver’s video recordings to detect drowsiness. These behavioral assessments include Eye Blinks detection, Yawning Detection, Percentage Eye Closure (PERCLOS) and Pose Estimation. All these are ensembled together to give a more accurate prediction of a driver being drowsy. It was concluded that the number of false positives increase during night-time and thus the accuracy of the system goes down when the lighting conditions are low. Also, camera for driver’s video recording should be placed just behind the left of steering wheel for maximum number of true detections. The system also works in realtime thus making it more useful.