Driver Drowsiness Detection System
Eshwar Enugurti, Sayali Ambekar, Rohit Dethe, Abhishek Benjamin, Prof. Madhavi Sadu
Open CV, Face Detection, Python, Alert, Blinking Eyes
Most fatalities and injuries among humans are caused by traffic accidents. According to the World Health Organisation, injuries from traffic accidents claim one million lives annually worldwide. When a driver is tired, sleep deprived, or both, they run the risk of falling asleep at the wheel and hurting other people as well as themselves. According to studies on auto accidents, driving while sleepy is a major contributing factor to major auto accidents. These days, it's found that driving while fatigued is the primary cause of drowsiness. Sleepiness is now the primary factor contributing to the rise in traffic accidents. This turns into a significant problem in the world that needs to be resolved right away. Enhancing real-time drowsiness 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 drowsiness. 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 tracking if the driver's eyes were open following the alert. We established a score that increased if the eyes were closed and decreased if they were open. With an accuracy of 80%, this paper aims to solve the issue of drowsiness detection and contribute to a decrease in traffic accidents.
Article Details
Unique Paper ID: 164546

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1680 - 1685
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