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.
Similar content being viewed by others
References
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
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
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
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
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
Noton D, Stark L. Scanpaths in saccadic eye movements while viewing and recognizing patterns. Vision Res, 1971, 11(9): 929–942
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
McCarthy D, Riegelsberger J, Sasse M A. Commercial uses of eye tracking. HCI Technical Report, 2005
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
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
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
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
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
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
Daugman J. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal, 1993, 15(11): 1148–1161
Nishino K, Nayar S K. Eyes for relighting. In: Proceeding of ACM SIGGRAPH 2004. Orlando: ACM 2004. 704–711
Chau M, Betke M. Real Time Eye Tracking and Blink Detection with USB Cameras. Boston University Computer Science Technical Report, 2005
Zhou D H, Xi Y G, Zhang Z J. A suboptimal multiple fading extended Kalman filter. Acta Autom Sinica, 1991, 17(6): 689–695
Zhou D H. Fault detection and diagnostics for a class of nonlinear systems. Dissertation for the Doctoral Degree. Shanghai: Shanghai Jiao Tong University, 1990
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
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
Zhou D H, Wang Q L. Strong tracking filter of nonlinear systems with colored noise. J Beijing Inst Tech, 1997, 17(3): 321–326
Author information
Authors and Affiliations
Corresponding authors
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)
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-009-0081-1