Driver Inattention Monitoring System Based on the Orientation of the Face Using Convolutional Neural Network
Prabuddha Manjula, Adarsh S R, K.I. Ramachandran · 2020
Driving inattentively is one of the prime reasons for vehicle accidents worldwide and has significant implications for road safety. A prompt alert to the inattentive driver can mitigate many accidents and save numerous lives, and reduce the cost of damages caused by accidents. To achieve this, a proposal of a nonintrusive and noninvasive driver inattention monitoring and alerting system in real time has been put forward. A mobile camera mounted on the windshield captures the video of the driver. Viola-Jones algorithm detects the face in each frame of the video and the Kanade-Lucas-Tomasi (KLT) algorithm tracks the detected face from one frame to another frame. The driver is classified as inattentive or attentive using Convolutional Neural Network (CNN). The transfer learning of the AlexNet Convolutional Neural Network architecture is adopted for the classification. The accuracy, precision, sensitivity, F1 score, and specificity of the system proposed in this paper are 98.24%, 100%, 96.47%, 98.21% and 100%, respectively.