基于强跟踪五阶容积卡尔曼滤波的眼睛跟踪算法

Eye tracking algorithm based on strong tracking fifth-degree cubature Kalman filter

摘要:

非侵入式眼睛跟踪在许多基于视觉的人机交互应用中扮演十分重要的角色，但由于眼睛运动的强非线性，如何确保眼睛跟踪过程中对外界干扰的鲁棒性以及跟踪精确度是其应用的关键问题。为提高眼睛跟踪的鲁棒性和精确度，提出强跟踪五阶容积卡尔曼滤波算法(ST-5thCKF)，将强跟踪滤波(STF)次优渐消因子引入具有接近最少容积采样点且保持五阶滤波精确度的五阶容积卡尔曼滤波(5thCKF)，获取5thCKF对强非线性良好滤波精确度同时具备STF对外界干扰的鲁棒性。真实条件下的实验结果验证了所提算法在眼睛跟踪中的有效性。

Abstract:

Non-intrusive eye tracking plays an important role in many vision-based human computer interaction applications. How to ensure the robustness of external interference and tracking precision during eye tracking is the key problem to its applications owing to the strong nonlinearity of eye motion. To improve the robustness and precision of eye tracking, the Strong Tracking fifth-degree Cubature Kalman Filter(ST-5thCKF) algorithm is proposed. The algorithm introduces the suboptimal fading factor of Strong Tracking Filter(STF) into fifth-degree Cubature Kalman Filter(5thCKF) which almost has the least cubature sampling points while maintaining the fifth-degree filtering accuracy. The proposed algorithm bears the high filtering precision to strong nonlinearity of 5thCKF, as well as the robustness to external interference of STF. The experimental results under practical conditions show the validity of the proposed algorithm in eye tracking.