Driver Drowsiness Detection System

Authors

  • Ambekar Shivam Nivrutti  Computer Department, Sanghavi College of Engineering, Nashik, Maharashtra, India
  • Korde Mohan Rajaram  Computer Department, Sanghavi College of Engineering, Nashik, Maharashtra, India
  • Patil Sagar Rajendra  Computer Department, Sanghavi College of Engineering, Nashik, Maharashtra, India
  • Puranik Shubham Dipak  Computer Department, Sanghavi College of Engineering, Nashik, Maharashtra, India
  • Prof. Pushpendu Biswas  Computer Department, Sanghavi College of Engineering, Nashik, Maharashtra, India
  • Prof. Bajirao Subhash Shirole  Computer Department, Sanghavi College of Engineering, Nashik, Maharashtra, India

Keywords:

Raspberry pi, Image processing, Frames, Pi Cam, Alert.

Abstract

A Drowsy Driver Detection System has been developed, using a non-intrusive machine vision based concepts. The system uses a small monochrome security camera that points directly towards the driver's face and monitors the driver's eyes in order to detect fatigue. In such a case when fatigue is detected, a warning signal is issued to alert the driver. This report describes how to find the eyes, and also how to determine if the eyes are open or closed. The algorithm developed is unique to any currently published papers, which was a primary objective of the project. The system deals with using information obtained for the binary version of the image to find the edges of the face, which narrows the area of where the eyes may exist. Once the face area is found, the eyes are found by computing the horizontal averages in the area. Taking into account the knowledge that eye regions in the face present great intensity changes, the eyes are located by finding the significant intensity changes in the face. Once the eyes are located, measuring the distances between the intensity changes in the eye area determine whether the eyes are open or closed. A large distance corresponds to eye closure. If the eyes are found closed for 5 consecutive frames, the system draws the conclusion that the driver is falling asleep and issues a warning signal. The system is also able to detect when the eyes cannot be found, and works under reasonable lighting conditions.

References

  1. W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 35, pp. 399-459, 2003.
  2. A Real Time Embedded System Application for Driver Drowsiness and Alcoholic Intoxication Detection by Dwipjoy Sarkar, Atanu Chowdhury M.Tech student, Assistant professor, Department of Electronics & Communication Engineering NIT Agartala, India Tripura, India .
  3. Nan-Ning Zheng,Shuming Tang,Hong Cheng and Qing Li,Guanpi Lai and Fei-Yue Wang,”Toward Intelligent Driver-Assistance and Safety Warning Systems”,Intelligent Transportation System,IEEE 2004.
  4. Subir Biswas, Raymond Tatchikou, Francois Dion “Vehicular to Vehicular Wireless Communication Protocols for Enhancing Highway Traffic Safety”, IEEE Communication Magazine, January 2006.
  5. Christian Scharfenberger, Samarjit Chakraborty, John Zelek and David Clausi”, Anti-Trap Protection for an Intelligent Smart Car Door System”,15th International IEEE Conference on Intelligent Transportation System, Anchorage, Alaska, USA, September 16-19, 2012.

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Published

2017-06-30

Issue

Section

Research Articles

How to Cite

[1]
Ambekar Shivam Nivrutti, Korde Mohan Rajaram, Patil Sagar Rajendra, Puranik Shubham Dipak, Prof. Pushpendu Biswas, Prof. Bajirao Subhash Shirole, " Driver Drowsiness Detection System, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 3, Issue 4, pp.286-289, May-June-2017.