March19 tun
Transcript of March19 tun
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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
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Introduction
Driver fatigue resulting from sleep deprivation or sleep disorders is an important factor in the increasing number of accidents on today's roads.
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Research Question
The main research question addressed.
How to detect driver fatigue in real-time by eye tracking?
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Challenges
Richness and complexity of facial expression
Fast head and eye movements Illumination interference
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Methodology: Overview Face Detection
Haar- Robustness Eye Location Geometric projection
Eye tracking Unscented Kalman filter
Driver Fatigue Detection Eye closed for 5 frames
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Methodology: 1. Face detection
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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
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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
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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
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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
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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.
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Methodology: 3. Eye Tracking1. Estimated state of the
system
2. Variance/uncertainty of the estimatestate transition
model
Kalman filter
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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
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Methodology: 3. Eye Tracking Unscented Kalman
filter
Observation noiseProcess noise
x- unobserved statey- observed state
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Methodology: 4. Fatigue Detection
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Data Collection, Processing
Pentium III 1.7G CPU with 128MB RAM
Video: Camera placed on the car dashboard
Input Video: 352 X 288
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Results
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Key Results
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Key Results
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Summary
Eye Tracking technique for Driver Fatigue Detection