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 robust- ness of external in...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 robust- ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today＇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.展开更多
基金Supported by the National Natural Science Foundation of China (Grant No. 60572027)the Outstanding Young Researchers Foundation of Sichuan Province (Grant No. 03ZQ026-033)+1 种基金the Program for New Century Excellent Talents in University of China (Grant No. NCET-05-0794)the Young Teacher Foundation of Mechanical School (Grant No. MYF0806)
文摘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 robust- ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today＇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.
