Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM). (2021) - Rajamohana, S.P.; Radhika, E.G.; Priya, S.; Sangeetha, S.
Abstract: In today's world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is essential to provide accurate and timely alerts to the driver. In the proposed system, hybrid approach of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Term Dependencies) is used to detect the driver's drowsiness. Video camera is used to track the facial image and eye blinks of the driver. The proposed system works in three main phases: In the First phase, driver's face image is Identified and observed using a web camera. In the Second phase, the eye image features are extracted using the Euclidean algorithm. During the third phase, the eye blinks are continually monitored. The final stage decides whether the measure in eye square is closed state or open state. When a driver falls asleep, there will be a warning message to alert the driver to prevent road accidents.