Xiaoyong Ye Franz Alexander Van Horenbeke David Abbott Wearable Eye Tracker Xiaoyong Ye Franz  Alexander Van Horenbeke David Abbott

Index Introduction Background Hardware Software Experimental Results System Design Algorithm Pupil Localization Ellipse Fitting Calibration Homographic Mapping Experimental Results Future Work

Introduction A complete system able to track the user’s eye and map the position of their pupil with the area at which they are looking at in the scene in front of them

Background Wearable Eye-Tracking information Who has done previous work What they have used Recent Methods used with eye tracker

Objectives Hardware Software Wearable Real-Time Low-Cost Accurate Light and Confortable Moveable eye-camera

Hardware Head-Mounted Gear Two Cameras: Scene Camera Eye Camera

Hardware Scene Camera Eye Camera Captures the scene in front of the user Captures the eye With 5 DOF with respect to the head Fixed to the head

System Design Eye Image Scene Image Yes Calibration Done? Pupil Localization No Ellipse Fitting Marker Detection Calculate Homography Ellipse Center Mapping

Pupil Localization Automatic Threshold (Modified Otsu’s Method) Image Morphology(Dilation, Erosion) Connected Components Analysis(Find Pupil) Pupil Center Estimation

Histogram of an Eye Image Background Pupil Graylevel Threshold

Pupil Localization Threshold Erosion Connect Components Pupil Detection Dilation Fill holes

Ellipse Fitting 1. Updating the pupil Center 2. Need 5 points for Fitting Ellipse model 3. RANSAC to deal with noisy points

Ellipse Fitting RANSAC method Edge Image Starburst Algorithm Feature Points RANSAC Ellipse Fitting

Calibration = * Relationship between Ellipse center to Scene Image Homography Pupil Center Scene Position

Solving for homographies X’ = Hx 8 degrees of freedom in 3 x 3 matrix H, so at least n = 8 pairs of points are sufficient to determine it Set up a system of linear equations: Ah = 0 where vector of unknowns h = [a,b,c,d,e,f,g,h]T Need at least 8 eqs, but the more the better… Solve for h. solve using least-squares

calibration method 1. Look at Scene Marker and Press corresponding number on keyboard, 2. Each marker press 2 to 3 times. 3. Randomly select 8 pairs of points to calculate Homography.(Repeatly) 3. Choose the best Homography matrix.

Mapping (x2, y2) (x1, y1)

Experimental Results Frame rate 25/second Accurate Pupil Ellipse Mapping error is low( 13 pixels in 640*480 image)

Demo Link http://www.youtube.com/watch?v=lBXLpsXBGOA&context=C25ea4ADOEgsToPDskIo6A6rLXR8eySvaEf82q6h

Future Work Hardware Software Lighter cameras Scene camera position Use corneal refletion Try different mapping techniques

Thank you!