Driver Drowsiness Detection System for Preventing Accidents using Visual Behavior and Machine Learning Algorithms

Abstract

In current days almost lot of  road accidents are occurred due to driver’s fault such as rash driving, not following rules and regulations, not following safety measures and lack of sleep. Drowsy driving is one of the major causes of road accidents and death. This drowsiness is seen in most of the drivers while they drive long distances and hence proper monitoring is required to identify such drivers. Till now there was no proper method or mechanism which can detect the driver’s fatigue and its becoming challenge topic and one of the active research areas. In primitive days, we try to apply some methods based on vehicle or behavioural or physiological based. But only few methods are intrusive and distract the driver, some require expensive sensors and data handling. This motivate me to design this model in which, a low cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In this project by monitoring Visual Behaviour of a driver with webcam and machine learning SVM (support vector machine) algorithm we are detecting Drowsiness of a driver. This model will use inbuilt webcam to read pictures of a driver and then using OPENCV and also SVM algorithm extract facial features from the picture and then check whether driver in picture is blinking his eyes for consecutive 20 frames or yawning mouth then application will alert driver with Drowsiness messages. Here we try to use One Class SVM and Two Class SVM Algorithm for pre-trained drowsiness model and then using Euclidean distance function we are continuously checking or predicting EYES and MOUTH distance closer to drowsiness, if distance is closer to drowsiness then application will alert driver. We propose an algorithm name to locate, track, and analyze both the drivers face and eyes to measure NPERCLOS (Novel percentage of eye closure), a scientifically supported measure of drowsiness associated with slow eye closure.