Innovation in technology has continually impacted our lives positively, be it VR-aided surgery or connected cars. However, as always, there is still room for improvement. Road accidents, for example, especially those caused by driver drowsiness, is one of the biggest killers today. In an attempt to address this problem with technology, this report proposes a system based on ‘Behavioral Metrics for Driver Drowsiness Detection’ and provides a solution which can bring down the fatality rates. By capturing visual information of the driver and using this behavioral data with Artificial Intelligence, we are developing a real-time drowsiness detection system based on Convolutional Neural Networks. Here, we create a classification network to determine if the driver’s eyes are closed or not and whether the driver is yawning or not. The proposed system captures the driver’s images and analyses eye and mouth positions, it then uses this data and our CNN model to build a fatigue detection system. The system continuously monitors the driver's state. As soon as the driver gives any indication of drowsiness, the system sounds the alarm to alert the driver.
This project was done in colaboration with Nidhi Uppoor.
Refer to the SRS document for further information