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ZutaoZHANG,JiashuZHANG
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(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu Sichuan 610031, China; Sichuan Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu Sichuan 610031, China)
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Received:April 18, 2008Revised:April 15, 2009
基金项目:This work was supported by the National Natural Science Foundation of China (No.60971104), the Program for New Century Excellent Talents in University of China (No.NCET-05-0794), and the Young Teacher Scientific Research Foundation of Southwest Jiaotong University (No.2009Q032).
A strong tracking nonlinear robust filter for eye tracking
Zutao ZHANG,Jiashu ZHANG
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu Sichuan 610031, China; Sichuan Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu Sichuan 610031, China)
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 pose the primary obstacle to the integration of eye movements into today’s interfaces. In this paper, we present a strong tracking unscented Kalman filter (ST-UKF) algorithm, aiming to overcome the difficulty in nonlinear eye tracking. In the proposed ST-UKF, the Suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related Kalman filter for eye tracking, the proposed ST-UKF has potential advantages in robustness and tracking accuracy. The last experimental results show the validity of our method for eye tracking under realistic conditions.
Key words:  Eye tracking  Strong tracking unscented Kalman filter (ST-UKF)  Unscented Kalman filter (UKF)  Strong tracking filtering (STF)