Smart Road Safety: An IoT Approach to Driver Drowsiness Detection and Prevention
DOI:
https://doi.org/10.48001/978-81-966500-7-0-12Keywords:
Video Input/Output, Facial Recognition, Face Detection, Driver’s Eye, Alert, Extracting Eye ImageAbstract
The main cause of fatalities and injuries among humans is traffic accidents. The World Health Organisation estimates that injuries sustained in automobile accidents claim the lives of one million people worldwide. When a driver is sleep deprived, worn out, or both, they are more prone to nod off at the wheel and hurt not only themselves but also other people. Sleepiness when driving is a major contributing cause to major traffic accidents, according to studies on the topic. These days, research indicates that the main factor contributing to drowsiness while driving is weariness. The main cause of the increase in traffic accidents these days is tiredness. This develops into a major issue for the world that requires immediate attention. Enhancing real-time sleepiness detection performance is the main objective of all devices. Numerous tools were created to identify drowsiness, and these tools rely on various artificial intelligence algorithms. Thus, another area of our research is driver drowsiness detection which uses facial recognition and eye tracking to determine a driver’s level of tiredness. The system compares the extracted eye image with the dataset. The system used the dataset to identify that it could alert the driver with an alarm if the driver’s eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver’s eyes were open following the alert.
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