This document proposes a real-time eye tracking method to detect driver fatigue. The method uses face detection with Haar features and integral images to locate the eyes. An unscented Kalman filter tracks eye movements to account for nonlinearities. Driver fatigue is detected if the eyes are closed for 5 consecutive frames. The method was tested by collecting dashboard camera videos of drivers and processing the videos on a Pentium III computer. Key results demonstrated the technique's ability to detect driver fatigue in real-time.

1. A New Real-Time Eye
Tracking for Driver
Fatigue Detection
Presenter: Yamin Tun
Zutao Zhang, Jiashu Zhang
2006 6th International Conference on ITS Telecommunications Proceedings

2. Introduction
 Driver fatigue resulting from sleep
deprivation or sleep disorders is an
important factor in the increasing number of
accidents on today's roads.

3. Research Question
The main
research
question
addressed.
 How to detect driver fatigue in real-
time by eye tracking?

4. Challenges
 Richness and complexity of facial
expression
 Fast head and eye movements
 Illumination interference

5. Methodology: Overview
 Face Detection
 Haar- Robustness
 Eye Location
 Geometric projection
 Eye tracking
 Unscented Kalman filter
 Driver Fatigue Detection
 Eye closed for 5 frames

6. Methodology: 1. Face detection

7. Methodology: 1. Face detection
Haar features
 Haar features ~ convolution kernels (locate features in the image)
 Slide across image dimensions under different scales
Haar features used in viola Jones Applying on a given
image
https://www.dropbox.com/s/17udeu1ojmq8bck/Ramsri_Face_detection_and_tracking.pptx

8. Methodology: 1. Face detection
Integral Image
 Integral Image- Sum of pixels above and to the left of (x,y)
Sum
abov
e and
to left
https://www.dropbox.com/s/17udeu1ojmq8bck/Ramsri_Face_detection_and_tracking.pptx

9. Methodology: 1. Face detection
Integral Image
 Efficiently compute sum of pixels in rectangular block
 Use only four values at the corners of the rectangle.
Integral image
Sum of all pixels in
D = 1+4-(2+3)
= A+(A+B+C+D)-(A+C+A+B)
= D
https://www.dropbox.com/s/17udeu1ojmq8bck/Ramsri_Face_detection_and_tracking.pptx

10. Methodology: 2. Eye location
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.85.6309&rep=rep1&type=pdf
Templates for Eye Tracking

11. Methodology: 3. Eye Tracking
 Kalman filter
 Statistically optimal estimator- Recursively infers
parameters of current state from indirect, uncertain, noisy
input observations of current and previous states.

12. Methodology: 3. Eye Tracking
1. Estimated state of the
system
2. Variance/uncertainty of
the estimate
state transition
model
 Kalman filter

13. Methodology: 3. Eye Tracking
 Previous method: Standard Kalman filter
 It assumes linear system with Gaussian
distributions.
 It uses IR illumination
 Proposed method: Unscented Kalman filter
proposed by Julier and Uhlmann
 Eye movement model has non-linearity (Spherical
to Cartesian coordinates)

14. Methodology: 3. Eye Tracking
 Unscented Kalman filter
Observation noiseProcess noise
x- unobserved state
y- observed state

15. Methodology: 4. Fatigue
Detection

16. Data Collection, Processing
 Pentium III 1.7G CPU with 128MB
RAM
 Video: Camera placed on the car
dashboard
 Input Video: 352 X 288

17. Results

18. Key Results

19. Key Results

20. Summary
Eye Tracking technique for Driver Fatigue
Detection