A Methodology for Driver Drowsiness Detection based on OpenCV and Dlib Libraries

The risk, danger, and often catastrophic consequences of sleepy driving are increasing at an unprecedented rate. This study aims to use physical changes in a driver’s facial characteristics to detect weariness (or drowsiness) with car motorist at an early stage with which the car motorist will take corrective action. This research aims to analyze the driving behavior in order to eliminate avoidable car accidents. In intelligent transportation systems, real-time driving behavior monitoring is critical. This type of monitoring improves traffic safety by lowering or eliminating the danger of potential traffic accidents. The vision-based technique, which includes video cameras for dangerous scenario identification, is innovative and widely employed in sensing driving environment. In this scenario, video camera photos of a driver can define its facial traits, such as head movements, eye state, and mouth state, and then detect a current level of weariness state. In this work, we use the built-in front-facing camera to continually watch driving face features and detect driver tiredness and distraction early. While driving, the risky situations are determined instant using the computer vision libraries OpenCV and Dlib.