Accident Avoidance System using Drowsiness Detection based on ML & IP

Accident Avoidance System using Drowsiness Detection based on ML & IP

Prof. Ruchira Pathak¹, Ajinkya Solanke², Veer Solanki³ , Siddesh Somani⁴ , Preet Nandeshwar⁵

Prof. Ruchira Pathak¹, Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Ambegaon, Pune, India ¹

Projecties^{2 3 4 5}, Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Ambegaon, Pune

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Abstract - Drowsy driving is the cause of many accidents. It is becoming one of the most common reasons for traffic accidents. Numerous accidents happen by drowsy driving. Drowsiness is responsible for more than 30% of all accidents. To avoid this, a system is needed that detects drowsiness and alerts the driver, saving the driver's life. We offer a strategy for detecting driver sleepiness in this research. A webcam is utilised to keep a constant eye on the driver. This model employs image processing algorithms that are primarily focused on the driver's face and eyes. The driver's face is extracted, and the algorithm predicts eye blinking from the eye region. To assess perclos, we utilize an algorithm to track and evaluate the driver's face and eyes. If the blinking rate is too fast, the system sounds an alert to the driver.

Keywords: Eye detection, Eye Tracking, Face Detection, Drowsiness, Distraction