Authors:
Yangyang Li
1
;
2
;
Lili Guo
1
;
Guangbin Sun
1
;
Rongrong Fu
3
;
Zhen Yan
1
and
Ji Liang
1
Affiliations:
1
Technological and Engineering Center for Space Utilization of Chinese Academy of Sciences, No. 9 Deng Zhuang South Rd., Beijing, 100094, China
;
2
University of Chinese Academy of Sciences, No. 19(A) Yuquan Rd., Beijing, 100049, China
;
3
Yanshan University, No. 438 West Hebei Avenue, Qinhuangdao, Hebei, 066004, China
Keyword(s):
Eye Tracking, Smooth Pursuit, Gaze Calibration, Visual Guidance, Neural Network.
Abstract:
Eye tracking calibration based on smooth pursuit has the characteristics of rapidity and convenience, but most smooth pursuit calibration methods are based on spontaneous and passive gazes. The spatial-temporal characteristics of the target movement can significantly affect the tracking performance, but few works have performed calibration considering the effects of both the spatial and temporal variance of the smooth pursuit target. Therefore, we proposed an off-line smoothing pursuit calibration featuring actively regulated speed under specially designed visual guidance paths. In our prelude experiments, we found that there was an obvious correlation between the eye movement velocity and the error of gaze point measurement. In particular, when the movement velocity of gaze exceeded 6°/s, the accuracy and precision of the eye-tracking system were obviously lower. Based on these findings, the visual guidance trajectory was regulated, with the speed kept below 6°/s. The smooth pursuit
calibration was combined with the neural network learning method. The results showed that the mean absolute error was reduced from 1.0° to 0.4°, and the full calibration process took only approximately 45 seconds.
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