An Improved Unscented Kalman Filtering Combined with Feature Triangle for Head Position Tracking

Abstract

Aiming at the problem of feature point tracking loss caused by large head rotation and facial occlusion in doctors, this paper designs a head-position-tracking system based on geometric triangles and unscented Kalman filtering. By interconnecting the three feature points of the left and right pupil centers and the tip of the nose, they form a coplanar triangle. When the posture of the doctor’s head changes due to rotation, the shape of the corresponding geometric triangle will also deform. Using the inherent laws therein, the head posture can be estimated based on changes in the geometric model. Due to the inaccurate positioning of feature points caused by the deflection of the human head wearing a mask, traditional linear Kalman filtering algorithms are difficult to accurately track feature points. This paper combines geometric triangles with an unscented Kalman Filter (UKF) to obtain head posture, which has been fully tested in different environments, such as different faces, wearing/not wearing masks, and dark/bright light via public and measured datasets. The final experimental results show that compared to the linear Kalman filtering algorithm with a single feature point, the traceless Kalman filtering algorithm combined with geometric triangles in this paper not only improves the robustness of nonlinear angle of view tracking but also can provide more accurate estimates than traditional Kalman filters.