Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach - Kekong, P.E; Ajah, I.A.; Chidiebere, U (International Journal of Computer Sciences and Engineering, 2021)
This work presents real time drowsy driver detection and monitoring system using deep learning based behavioral approach. The aim is to design and implement software which captures live driver’s behavior during driving and train using convolutional neural network (CNN) to predict the behavior’s of the driver. This was achieved developing a drowsy driver dataset; intelligent video based device and the CNN architecture and configurations. The designs were implemented using deep learning tool and MATHLAB. The system was tested and the result showed a detection accuracy of 99.8%. MATHLAB was used to develop a prototype model of the system.