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Title : Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm

Author : Kumbha Ramu, Deverakonda Mallikerjuna, P.Goutham Kumar, Shaik Abeed Basha

Abstract :

The number of cars has increased exponentially during the past ten years. However, there has also been a direct correlation between this increase and accidents involving driving. According to research, a large number of these collisions are caused by drivers who are either preoccupied or who become tired during extended drives. Numerous image processing-based methods have been developed to identify a driver's level of exhaustion and drowsiness. These methods aren't always the greatest, though, as some could produce accurate yet incorrect forecasts.The EAR (Eye Aspect Ratio) Algorithm will be used in this study to develop a system that will determine whether or not the driver is feeling sleepy based on a live visual feed. Additionally, the system will notify the inattentive motorist of any potential collision utilizing Internet of Things sensors.

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International Journal of Engineering Research & Informatics (IJERI)
E-ISSN: 2348-6481

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