Autocalibration-based partioning relationship and parallax relation for head-mounted eye trackers

This paper presents new methods to calibrate a head-mounted Eye Tracker (ET) automatically, as well as a new way to obtain an estimated point of regard (POR), taking account of the parallax. Calibration is performed in real time; it is easy for the user who just needs to look at one calibration pattern for a few seconds before starting. This method provides a very important couple of points which helps to use a local relationship to compute the POR instead of a global one. This approach significantly improves the precision of the points of regard when the scene camera is mounted with a short focal lens. An estimation of POR when the user looks somewhere outside the calibration distance is also proposed. This estimation is based on an ET modelling such as a stereovision system, to take account of the parallax effect. The aim of this study is to simplify the use of ET techniques for “non-initiated” people, especially here learner drivers.

[1]  Dan Witzner Hansen,et al.  Homography normalization for robust gaze estimation in uncalibrated setups , 2010, ETRA.

[2]  Géry d'Ydewalle,et al.  A comparison of a new limbus tracker, corneal reflection technique, Purkinje eye tracking and electro-oculography. , 1993 .

[3]  Clarence E. Rash,et al.  A 25-year retrospective review of visual complaints and illusions associated with a monocular helmet-mounted display , 2008, Displays.

[4]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[5]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[6]  Dongheng Li,et al.  Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  Rafael Cabeza,et al.  Eye tracking: Pupil orientation geometrical modeling , 2006, Image Vis. Comput..

[9]  Peter F. Sturm,et al.  07 - Géométrie d’images multiples pour des modèles de caméras généraux , 2005 .

[10]  M. Basset,et al.  Visual characterization the road driver's behaviour , 2005, IEEE International Workshop on Intelligent Signal Processing, 2005..

[11]  Franklin C. Crow,et al.  Summed-area tables for texture mapping , 1984, SIGGRAPH.

[12]  Aude Billard,et al.  Calibration-Free Eye Gaze Direction Detection with Gaussian Processes , 2008, VISAPP.

[13]  Christophe Cudel,et al.  Study on the interest of hybrid fundamental matrix for head mounted eye tracker modeling , 2011, BMVC.

[14]  Gintautas Daunys,et al.  Investigation of Calibration Techniques in Video Based Eye Tracking System , 2008, ICCHP.

[15]  Thomas S. Tullis,et al.  Generation Y, web design, and eye tracking , 2010, Int. J. Hum. Comput. Stud..

[16]  D. Sliney,et al.  Safety with Lasers and Other Optical Sources , 1980, Springer US.

[17]  Laura Chamberlain Eye Tracking Methodology; Theory and Practice , 2007 .

[18]  Marcela Fejtová,et al.  AI Support for a Gaze Controlled Wheelchair , 2008 .

[19]  I Ketut Gede Darma Putra,et al.  Low-Cost Based Eye Tracking and Eye Gaze Estimation , 2011 .

[20]  Howell Istance,et al.  User performance of gaze-based interaction with on-line virtual communities. , 2008 .

[21]  Anand K. Gramopadhye,et al.  Binocular eye tracking in VR for visual inspection training , 2001, VRST '01.

[22]  ICNIRP STATEMENT ON LIGHT-EMITTING DIODES (LEDs) AND LASER DIODES: IMPLICATIONS FOR HAZARD ASSESSMENT , 2000, Health physics.

[23]  Karen E. Adolph,et al.  Visually guided navigation: Head-mounted eye-tracking of natural locomotion in children and adults , 2010, Vision Research.

[24]  S. Kanagasabay Safety with Lasers and Other Optical Sources—A Comprehensive Handbook , 1981 .

[25]  Thomas Martinetz,et al.  A software framework for simulating eye trackers , 2008, ETRA.

[26]  Helge J. Ritter,et al.  An Artificial Neural Network for High Precision Eye Movement Tracking , 1994, KI.

[27]  Stefan Kohlbecher,et al.  A novel approach to video-based pupil tracking , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[28]  D. Gennery,et al.  Calibration and Orientation of Cameras in Computer Vision , 2001 .

[29]  Bernhard Nebel,et al.  KI-94: Advances in Artificial Intelligence , 1994, Lecture Notes in Computer Science.