Real-time classification for autonomous drowsiness detection using eye aspect ratio

Abstract In recent years, driving accidents have been increasing gradually, with most of them due to drowsiness. To tackle this problem, various methods and systems were developed to alert the driver behind the wheels, and to prevent accidents. A few approaches include heartbeat monitoring, pulse rate check, blink, and yawn detector. This paper discusses a driver mechanism, which is capable of detecting fatigue in real-time, using the Eye Aspect Ratio – EAR – parameter, and also provides a database with an interface for managers to track drivers. When the driver is tired, an email will also be sent to the manager. On the drivers’ side, there will be a buzzer noise to wake the driver up and nearby resting locations will be shown to the driver. Further, mapping to the nearby resting location of the driver’s choice is shown, if the driver feels too drowsy and feels the need for rest. It is essential to fabricate and construct this complete system via IoT and OpenCV methods to address this issue.

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Driver’s Drowsiness Detection Using Dlib and IOT

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666201218162536 ◽

2020 ◽

Vol 10 ◽

Author(s):

Prasanna Lakshmi Kompalli ◽

Padma Vallakati ◽

Ganapathi Raju Nadimpalli ◽

Vinod Mahesh Jain ◽

Samuel Annepogu

Keyword(s):

Aspect Ratio ◽

The Other ◽

Human Intervention ◽

Road Accidents ◽

Online Content ◽

Facial Landmarks ◽

Drowsiness Detection ◽

Rear Part ◽

Cause Of Deaths ◽

Published Paper

Background: Road accidents are major cause of deaths worldwide. This is enormously due to fatigue, drowsiness and microsleep of the drivers. This don’t just risk the life of driver and copassengers but also a great threat to the vehicles and humans moving around that vehicle. Methods: Research, online content and previously published paper related to drowsiness are reviewed. Using the facial landmarks DAT file, the prototype will locate and get the eye coordinates and it will calculate Eye Aspect Ratio (EAR). The EAR indicates whether the driver is drowsy or not based on the result various sensors gets activated such as Alarm generator, LED Indicators, LCD message scroll, message sent to owner and engine gets locked. Results: The prototype is able to locate eyes in the frame and detect whether the person is sleepy or not. Whenever the person is feeling drowsy alarm gets generated in the cabinet on further if the person is feeling drowsy, LED indicators will start glowing, messaging will be scrolling at the rear part of vehicle so that other vehicles and humans gets cautioned and vehicle slows down and engine gets locked. Conclusion: This prototype will help in reduction of road accidents due to human intervention. It is not only helpful to the person who install it in their vehicles but also for the other vehicles and humans moving around it.

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