Citation

Walke KG, Shete H, Asknani D, Gadekar R, Mulani A. Int. J. Res. Appl. Sci. Eng. Technol. 2021; 9(12): 1850-1854.

Copyright

(Copyright © 2021, IJRASET)

DOI

10.22214/ijraset.2021.39236

PMID

unavailable

Abstract

We proposed to use this system to minimise the frequency of accidents caused by driver exhaustion, hence improving road safety. This device uses optical information and artificial intelligence to identify driver sleepiness automatically. We use Softmax to find, monitor, and analyse the driver's face and eyes in order to calculate PERCLOS (% of eye closure). It will also employ alcohol pulse detection to determine whether or not the person is normal. Due to extended driving durations and boredom in crowded settings, driver weariness is one of the leading causes of traffic accidents, particularly for drivers of big vehicles (such as buses and heavy trucks).
Keywords: Driver Drowsiness, OpenCV, TensorFlow, Image Processing, Computer Vision

Language: en