Road transportation is one of the primary mode of transportation, used by people to travel on a daily basis. Since this mode of transportation is cost-effective, it also makes drivers to transport goods and commodities in highways, especially during night time. These factors result in a profound amount of road accidents. Road accidents remains to be a global concern resulting in innumerable fatalities and injuries. Many of these accidents are caused by driver drowsiness, which is a major issue that requires immediate actions. Using machine learning technology, camera can be used to capture and monitor various driver-related parameters including eye movement and mouth movement to predict the drowsiness and yawning state of the driver. Dlib's pre-trained model is used to detect the facial features of the driver. The pre-trained model use CNN algorithm internally to detect the face. The Mouth Aspect Ratio (MAR) and Eye Aspect Ratio (EAR) is calculated and accordingly alerting the driver to prevent road accidents manifold.
Deep Learning Based Smart Alert System for Driver Drowsiness
2023-12-14
535218 byte
Conference paper
Electronic Resource
English
DDYDAS: Driver Drowsiness, Yawn Detection and Alert System
Springer Verlag | 2021
|Driver Drowsiness Detection Using Deep Learning
IEEE | 2021
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