CHAUFFEUR EXHAUSTION TRACKING SYSTEM USING MACHINE LEARNING

CHAUFFEUR EXHAUSTION TRACKING SYSTEM USING MACHINE LEARNING

Vishnu Teja R, Vamsi Krishna V, Abhiram p, Chethan Prasad P, Mrs. Anitha Lakshmi (Assistant Professor)

Dept. of Computer Science & Engineering

Prathyusha Engineering College

Tiruvallur, Tamilnadu

tejavishnu279@gmail.com

Abstract - These days, a growing number of professions demand sustained focus. Chauffeurs need to pay attentive attention to the road so they can respond quickly to unexpected incidents. The primary factor in numerous traffic accidents is frequently chauffeur weariness. Consequently, it is necessary to create a system that can identify drowsiness in a chauffeur and alert them to it, which might greatly reduce the frequency of accidents caused by weariness. The development of such systems, however, has numerous challenges when it comes to accurately and promptly identifying a chauffeur's signs of sleepiness. The employment of a vision-based technique is one of the technical options for implementing chauffeur drowsiness tracking systems. This project recognizes the chauffeur's tiredness and sends a warning sound to remind him to stay awake. Here, we estimate the chauffeur's vision system in order to determine his level of tiredness.

Index Term – Chauffeur Drowsiness Tracking, Eye

Tracking System, Exhaustion, Convolutional Neural

Network