Driver Drowsiness Detection using Hybrid Algorithm

U. Poorna Lakshmi, P.V.S. Srinivas, S Shyam, Mallikarjun Reddy Muchintala, Viswanath Reddy Palugulla, Hemanth Yadav Mandra

Abstract


In this work we focus on the discernment of sleepiness in drivers’ drowsiness proposing a hybrid algorithm which aims to confirm whether the driver's level of attention has decreased owing to a nap or any other medical issue, such as brain problems. Therefore, the proposed hybrid algorithm uses both Haarcascade classifier and Convolutional Neural Network (CNN) algorithm to detect drivers’ drowsiness. The driver's eyes will be monitored and an alert sound will be generated by Raspberry Pi module, but the face must be moving in real time, and the aspect ratio must be between 16:9 and 1.85:1. People often feel sleepy since activities like driving call for a proper mental state, and bad work-life balance has additional negative repercussions. When we give input through normal camera it analyses drivers state of eyes and mouth, actually it checks aspect ratio of eye. We proved in comparative trials that our hybrid algorithm beats current driving fatigue detection algorithms in speed as well as accuracy.

Keywords


Convolutional Neural Network (CNN); Drowsiness Detection; Haar cascade Classifier; OpenCV

References


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