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A New Real-Time Eye Tracking for DriverFatigue Detection
Presenter: Yamin Tun
Zutao Zhang, Jiashu Zhang
2006 6th International Conference on ITS Telecommunications Proceedings
Introduction
Driver fatigue resulting from sleep deprivation or sleep disorders is an important factor in the increasing number of accidents on today's roads.
Research Question
The main research question addressed.
How to detect driver fatigue in real-time by eye tracking?
Challenges
Richness and complexity of facial expression
Fast head and eye movements Illumination interference
Methodology: Overview Face Detection
Haar- Robustness Eye Location Geometric projection
Eye tracking Unscented Kalman filter
Driver Fatigue Detection Eye closed for 5 frames
Methodology: 1. Face detection
Methodology: 1. Face detectionHaar 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
Methodology: 1. Face detectionIntegral Image
Integral Image- Sum of pixels above and to the left of (x,y)
Sum above and to left
https://www.dropbox.com/s/17udeu1ojmq8bck/Ramsri_Face_detection_and_tracking.pptx
Methodology: 1. Face detectionIntegral 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
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
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.
Methodology: 3. Eye Tracking1. Estimated state of the
system
2. Variance/uncertainty of the estimatestate transition
model
Kalman filter
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) No IR illumination needed
Methodology: 3. Eye Tracking Unscented Kalman
filter
Observation noiseProcess noise
x- unobserved statey- observed state
Methodology: 4. Fatigue Detection
Data Collection, Processing
Pentium III 1.7G CPU with 128MB RAM
Video: Camera placed on the car dashboard
Input Video: 352 X 288
Results
Key Results
Key Results
Summary
Eye Tracking technique for Driver Fatigue Detection
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