Real Time Detection System of Driver Fatigue

Main Article Content

Pranali Shelke
Shraddha Magar
Rutuja Jawkar

Abstract

The leading cause of vehicle crashes and accidents is the driver distraction. With the rapid development of motorization, driver fatigue has become a very serious traffic problem. Reasons for traffic accidents are driving after alcohol consumption, driving at night, driving without taking rest, aging, sleepiness, and fatigue occurred due to continuous driving, long working hours and night shifts. So to reduce rate of accidents due to above reasons, is aim of this project. This paper presents a method for detection of early signs of fatigue using feature extraction, Haar classifier and delivering of information and whereabouts of the driver to the emergency contact numbers.

Article Details

How to Cite
[1]
Pranali Shelke, Shraddha Magar, and Rutuja Jawkar, “Real Time Detection System of Driver Fatigue”, Int. J. Comput. Eng. Res. Trends, vol. 3, no. 4, pp. 197–198, Apr. 2016.
Section
Research Articles

References

Driver Fatigue Detection System, IEEE Std. 224441521.

The Real-Time Detection System of Driver Distraction Using Machine Learning Fabio Tango and Marco Botta, June2013.

Kusuma Kumari B. M Review on Drowsy Driving: Becoming Dangerous Problem International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064. Volume 3 Issue 1, January 2014.

http://opencv.org/

Luis Miguel Bergasa, Maria Elena Lopez Guilen,” Driver Fatigue Detection System”, IEEE Std. 224441521. March 2009.

FeiWang, ShaomanWang, XihuiWang, Ying Peng, Yiding Yang,”Design Of Driving Fatigue Detection System Based On The Hybrid Measures UsingWavelet-Packets Transform” IEEE International Conference on Robotics Automation, May 31- June 7,2014.

B. Ye Sun, Student Member, IEEE, and Xiong (Bill) Yu,”An Innovative Nonintrusive Driver Assistance System for Vital Signal Monitoring” Nov-2014.

Chi Zhang, Hong Wang, and Rongrong Fu ,”Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures”