March19 tun

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


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


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

Science

March19 tun