SMART SURVEILLANCE: AI-DRIVEN DRIVER DROWSINESS DETECTION SYSTEM

Authors

  • Yashika Khandelwal Student, Department of Information Technology, Poornima College of Engineering, Sitapura, Rajasthan
  • Palak Gupta Student, Department of Information Technology, Poornima College of Engineering, Sitapura, Rajasthan
  • Priyal Jain Student, Department of Information Technology, Poornima College of Engineering, Sitapura, Rajasthan
  • Vartika Jain Student, Department of Information Technology, Poornima College of Engineering, Sitapura, Rajasthan
  • Shazia Haque Assistant Professor, Department of Information Technology, Poornima College of Engineering, Sitapura, Rajasthan

DOI:

https://doi.org/10.37628/ijcam.v9i1.1556

Keywords:

Driver Drowsiness Detection, Eye Aspect Ratio, Artificial Intelligence, Machine Learning, Deep Learning

Abstract

In this study, a real-time application for driver drowsiness detection based on machine learning and deep learning is developed. Accidents, including traffic accidents, are now a frequent cause of fatalities and injuries among people. One of the major contributing factors to traffic accidents and a significant threat to road safety is drowsy driving. As a result, there are more people dying and getting hurt every year. Serious traffic accidents have frequently resulted from fatigued driving. Such accidents can be avoided by detecting driver intoxication early and acting accordingly. When a driver is drowsy, they are halfway between asleep and awake. In addition to addressing all of these accident causes, the goal of this study is to resolve this issue. A system for the early detection of drowsiness symptoms must be created in order to prevent traffic accidents. Detected symptoms can be either psychological or physical. One of the IT industry's fastest-growing subsectors is artificial intelligence. The study of artificial intelligence focuses on how to teach a computer to perform tasks that humans currently perform. With Python3, we will create a machine learning and deep learning based artificial intelligence model for facial recognition

References

. Shashikant Singh, Rana Pratap Singh,“Implementation Driver Drowsiness Detection using ML/DL”,2022, IEEE

. Aneesa Al Redhaei, Yaman Albadawi, Safia Mohamed, Ali Alnoman,“Realtime Driver Drowsiness Detection using Machine Learning”,2022, IEEE

. Harshit Meda, Jnapareddy Mohan Padmanabha Ganesh, Ashish Sahani,“Machine Learning Models for Drowsiness Detection”,2021,IEEE

. Cyun-Yi Lin, Paul Chang, Alan Wang, and ChihPeng Fan,“Machine Learning and Gradient Statistics Based Real-Time Driver Drowsiness Detection”,2018, IEEE

. Ayman Altameem, Ankit Kumar, Ramesh Chandra Poonia, Sandeep Kumar, Abdul Khader Jilani Saudagar, “Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning”,2021, IEEE

. Prasath N, Sreemathy J, Vigneshwaran P,“Driver Drowsiness Detection Using Machine Learning Algorithm”,2022,IEEE

. Aswathi CD, Nimmy Ann Mathew, Riyas K S, Renu Jose,“Comparison Of Machine Learning Algorithms For Heart Rate Variability Based Driver Drowsiness Detection”,2021, IEEE

. Hafsa Sidaq, Faisal Bukhari, Waheed Iqbal,“Automatic Driver Alertness using Machine Learning”,2021, IEEE

. Renju Rachel Varghese, Pramod Mathew Jacob, Joanna Jacob, Merlin Nissi Babu, Rupali Ravikanth, Stephy Mariyam George,“An Integrated Framework for Driver Drowsiness Detection and Alcohol Intoxication using Machine Learning”,2021, IEEE

. Sukrit Mehta, Parimal Mishra, Arpita Jadhav Bhatt, Parul Agarwal,“AD3S: Advanced Driver Drowsiness Detection System using Machine Learning”,2019, IEEE

Downloads

Published

2023-11-17

Issue

Vol. 9 No. 1 (2023)

Section

Articles