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Driver Fatigue Detection System

ppt made for NCETE conference

Jitendra Chauhan

on 30 November 2014

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Transcript of Driver Fatigue Detection System

Driver Fatigue Detection System

Prof. Nazneen Pendhari

Jitendra Singh Chauhan

Zainab Fatakdawala

Junaid Kundlik
Significance of the Problem
Fatigue leads to approximately 40% of crashes on highways.

Fatigue is the most frequent contributor to crashes.
A drowsy/sleepy driver is unable to determine when he/she will have an uncontrolled sleep onset.

Fall asleep crashes are very serious in terms of injury severity.

Driver drowsiness related accidents has a high fatality rate.

It reduces the risk of a accident.
Significance of the Problem (cont...)
Driver drowsiness is the leading causal factor in traffic accidents.

Project describes a method to monitor driver safety.

Driving performance deteriorates with increased drowsiness.
A monitoring system is designed using MATLAB.

Processes the data to indicate the current driving aptitude of the driver.

Warning alarm is raised if driver fatigue.
To take input image through a web camera.

To detect face by implementing appropriate algorithms.

To detect Region of Interest(ROI).

To detect eye from the ROI.
To obtain highest possible accuracy through improved algorithms.

To detect driver drowsiness (if any) by monitoring the eye blink rate.

To alarm the driver if drowsy.
Software Specifications
Image processing using MATLAB 7 or higher.

Hardware Specifications
Suitable Webcam.
Processor: Pentium 4 or higher
RAM: 1 GB (minimum) or more
Memory: 10 GB.
Record video

Convert into frames

RGB to Gray scale conversion of images

Histogram Equalization

Edge Detection
Circular Hough transform

Detect eye region.

Extract radii.

Histogram Equalization of eye.

Compare with threshold value.

Alarm the driver.

Real time.


Prevent Accidents.

Cost benefit considerations.

Overcoming the false alarm problem.

System response/Driver interface.
Integrating the algorithm into mobile devices.

Adding onboard GSM module for SMS alert
Future Work
Fatigue detection considering Driving styles and environment.

Change the windscreen view as per driver’s state
Highly accurate and reliable detection of drowsiness.

Non-invasive approach without annoyance and interference.

Judge the driver’s alertness level on the basis of continuous eye closure.

Alarm the driver to prevent accidents.
[1] Anil K. Jain, “Fundamentals of Image Processing”, PHI.
[2] R. C. Gonzalez R.E.Woods, “Digital Image Processing”, Second edition, Pearson Education.

Conference Papers:
[3] Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals - by Boon-Giin Lee and Wan-Young Chung, Member, IEEE. (IEEE SENSORS JOURNAL, VOL. 12, NO. 7, JULY 2012)
[4] Detection of Driver Fatigue Caused by Sleep Deprivation - by Ji Hyun Yang, Zhi-Hong Mao, Member, IEEE, Louis Tijerina, Tom Pilutti, Joseph F. Coughlin, and Eric Feron. ( IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 39, NO. 4, JULY 2009)
[5] Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue - by Qiang Ji, Zhiwei Zhu, and Peilin Lan (IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 4, JULY 2004)
[6] Review of on-road driver fatigue monitoring devices - by Ann Williamson and Tim Chamberlain (April, 2004)
[7] Video sensor based eye tracking and blink detection to automated drowsy driving warning system using image processing -by Y. S. Lee and W. Y. Chung (Proc. 13th Int. Meet. Chem. Sensors, Perth, Australia, Jul. 2010, p. 358.)
[8] Development of drowsiness detection system - by H. Ueno, M. Kaneda, and M. Tsukino (Proc. Vehicle Navigation Information Systems Conf., Yokohama, Japan, Aug. 1994, pp. 15–20)

[9] http://www.google.com
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