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A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking

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Abstract

Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robustness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into todays’s interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty in modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.

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References

  1. Ji Q, Zhu Z W, Lan P L. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Trans Veh Technol, 2004, 53(4): 1052–1068

    Article  Google Scholar 

  2. Horng W B, Chen C Y, Chang Y, et al. Driver fatigue detection based on eye tracking and dynamic template matching. In: Proceeding of the 2004 IEEE International Conference on Networking, Sensing & Control. Taipei: IEEE Press, 2004. 7–12

    Chapter  Google Scholar 

  3. Dong W H, Wu X J. Driver fatigue detection based on the distance of eyelid. In: Proceedings of the VLSI Design & Video Technology. Suzhou: IEEE Press, 2005. 365–368

    Chapter  Google Scholar 

  4. Majaranta P, Raiha K. Twenty year of eye typing: system and design issues. In: Proceedings of ACM Eye Tracking Research and Applications Symposium. New Orleans: ACM, 2002. 15–22

    Chapter  Google Scholar 

  5. Laura A, Joachims T. Eye-tracking analysis of user behavior in WWW search. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, Sheffield: ACM, 2004. 478–479

    Google Scholar 

  6. Noton D, Stark L. Scanpaths in saccadic eye movements while viewing and recognizing patterns. Vision Res, 1971, 11(9): 929–942

    Article  Google Scholar 

  7. Takehiko O, Naoki M, Shinjiro K. Just blink your eyes: a head-free gaze tracking system. In: Conference on Human Factors in Computing Systems. Lauderdale: ACM, 2003. 115–122

    Google Scholar 

  8. McCarthy D, Riegelsberger J, Sasse M A. Commercial uses of eye tracking. HCI Technical Report, 2005

  9. Li D H, Winfield D, Parkhurst D J. A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRs’05). San Diego: IEEE Press, 2005. 79–86

    Google Scholar 

  10. Zhu J, Yang J. Subpixel eye gaze tracking. In: Proceedings of 2002 IEEE International Conference on Automatic Face and Gesture Recognition. Washington: IEEE Press, 2002. 124–129

    Google Scholar 

  11. Ohno T, Mukawa N, Yoshikawa A. Freegaze: a gaze tracking system for everyday gaze interaction. In: Proceedings of Eye Tracking Research and Applications Symposium. Louisiana: ACM, 2002. 15–22

    Google Scholar 

  12. Zhu Z W, Ji Q, Fujimura K. Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination. In: Proceedings of International Conference on Pattern Recognition. Canada: IEEE Press, 2002. 318–321

    Google Scholar 

  13. Ebisawa Y. Unconstrained pupil detecting technique using two light sources and the image difference method. In: Proceedings of Visualization and Intelligent Design in Engineering and Architecture. Southampton: Computational Mechanics Publications, 1995. 79–89

    Google Scholar 

  14. Morimoto C H, Flickner M. Real-time multiple face detection using active illumination. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble: IEEE Press, 2000. 8–13

    Google Scholar 

  15. Daugman J. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal, 1993, 15(11): 1148–1161

    Article  Google Scholar 

  16. Nishino K, Nayar S K. Eyes for relighting. In: Proceeding of ACM SIGGRAPH 2004. Orlando: ACM 2004. 704–711

    Chapter  Google Scholar 

  17. Chau M, Betke M. Real Time Eye Tracking and Blink Detection with USB Cameras. Boston University Computer Science Technical Report, 2005

  18. Zhou D H, Xi Y G, Zhang Z J. A suboptimal multiple fading extended Kalman filter. Acta Autom Sinica, 1991, 17(6): 689–695

    MATH  Google Scholar 

  19. Zhou D H. Fault detection and diagnostics for a class of nonlinear systems. Dissertation for the Doctoral Degree. Shanghai: Shanghai Jiao Tong University, 1990

    Google Scholar 

  20. Zhou D H, Sun Y X, Xi Y Z, et al. Extension of Friedlands’s separate-bias estimation to randomly time-varying bias of nonlinear systems. IEEE Trans Autom Contr, 1993, 38(8): 1270–1273

    Article  MATH  MathSciNet  Google Scholar 

  21. Fan W B, Liu C F, Zhang S Z. Improved method of Strong tracking extended Kalman filter. Contr and Dec, 2006, 21(1): 73–76

    MATH  MathSciNet  Google Scholar 

  22. Zhou D H, Wang Q L. Strong tracking filter of nonlinear systems with colored noise. J Beijing Inst Tech, 1997, 17(3): 321–326

    MATH  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    ZuTao Zhang & JiaShu Zhang

  2. Sichuan Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu, 610031, China

    ZuTao Zhang

Authors
  1. ZuTao Zhang
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  2. JiaShu Zhang
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Corresponding authors

Correspondence to ZuTao Zhang or JiaShu Zhang.

Additional information

Supported by the National Natural Science Foundation of China (Grant No. 60572027), the Outstanding Young Researchers Foundation of Sichuan Province (Grant No. 03ZQ026-033), the Program for New Century Excellent Talents in University of China (Grant No. NCET-05-0794), and the Young Teacher Foundation of Mechanical School (Grant No. MYF0806)

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Zhang, Z., Zhang, J. A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking. Sci. China Ser. F-Inf. Sci. 52, 688–694 (2009). https://doi.org/10.1007/s11432-009-0081-1

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  • DOI: https://doi.org/10.1007/s11432-009-0081-1

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