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Real Time Drivers Drowsiness Detection and alert System by Measuring EAR

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Rajneesh, Anudeep Goraya, Gurmeet Singh
10.5120/ijca2018918055

Rajneesh, Anudeep Goraya and Gurmeet Singh. Real Time Drivers Drowsiness Detection and alert System by Measuring EAR. International Journal of Computer Applications 181(25):38-45, November 2018. BibTeX

@article{10.5120/ijca2018918055,
	author = {Rajneesh and Anudeep Goraya and Gurmeet Singh},
	title = {Real Time Drivers Drowsiness Detection and alert System by Measuring EAR},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2018},
	volume = {181},
	number = {25},
	month = {Nov},
	year = {2018},
	issn = {0975-8887},
	pages = {38-45},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume181/number25/30095-2018918055},
	doi = {10.5120/ijca2018918055},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

On road driver’s fatigue and drowsiness is contributing more than 30%[1] of reported road accidents. Driver drowsiness can be estimated by monitoring biomedical signals ,visual assessment of driver’s bio-behavior from face images , by monitoring drivers performance or by combines all above techniques. Proposed algorithm is based on live monitoring of EAR (Eye aspect Ratio) by application of Image processing. HD live video is decomposed in continues frames and facial landmarks has been detected using pre trained Neural Network based Dlib functions. Dlib functions are trained using HAAR Cascade algorithm. Intel’s Open source Image processing libraries (OPEN CV) is used as primary Image processing tool. Python Language is used as main codding language. EAR is calculated by calculating Euclidean distance between measured eye coordinates . Blink and microsleep detection mechanism is implemented by monitoring EAR against a threshold value. Blinks and drowsiness level are displayed on monitor screen with microsleep detection audio warning .

References

  1. Association for Safe International Road Travel (ASIRT), Road Crash Statistics.http://asirt.org/initiatives/informing-roadusers/road-safety-facts/road-crash-statistics, 2016
  2. https://en.wikipedia.org/wiki/Microsleep
  3. Journal of VLSI Signal Processing 23, 497–511 (1999) c °1999 Kluwer Academic Publishers. Manufactured in The Netherlands.
  4. https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html
  5. Eye Detection Using Morphological and Color Image Processing Tanmay Rajpathaka, Ratnesh Kumar and Eric Schwartzb
  6. A Robust Algorithm for Eye Detection on Grey Intensity Face without Spectacles- JCS&T Vol. 5 No. 3
  7. Froba Kebbuck: Audio- and Video-Based Biometric Person Authentication, 3rd International Conference, AVBPA 2001, Halmstad, Sweden, June 2001. Proceedings, Springer. ISBN 3-540-42216-1.
  8. Driver Drowsiness Detection using Eye-Closeness Detection (2016 12th International Conference on Signal-Image Technology & Internet-Based Systems)

Keywords

EAR, Microsleep, Drowsiness, OPENCV, Dlib, Python.