Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

Abstract: Drowsiness is among the primary reasons for driver caused traffic accidents. The interactive systems that have been designed to minimize road accidents by notifying the drivers are referred to as Advanced Driver Assistance Systems (ADAS). Most significant ADAS include Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In the current research, an eye state detection based ADAS system is introduced to identify driver drowsiness. To start, Viola-Jones algorithm method is utilized for identifying the face and eye regions in the current work. The eye region, detected in the present method, is classified into open or closed through utilization of a machine learning approach. Ultimately, eye conditions are inspected at time domain using percentage of eyelid closure (PERCLOS) metric and drowsiness states are calculated by Support Vector Machine (SVM). The above proposed methods are tested on 7 real individuals and drowsiness conditions are detected better accuracy, respectively.

Driver Drowsiness Detection, Advanced Driver Assistance Systems (ADAS), Viola-Jones Algorithm, Eye State Detection, Face and Eye Detection, Machine Learning, Support Vector Machine (SVM).

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