  For a humanoid robot to make eye contact and initiate communication with a
person, it is necessary to estimate the person's head position. However, eye
contact becomes difficult due to the mechanical delay of the robot when the
person is moving. Owing to these issues, it is important to conduct a
head-position prediction to mitigate the effect of the delay in the robot
motion. Based on the fact that humans turn their heads before changing
direction while walking, we hypothesized that the accuracy of three-dimensional
(3D) head-position prediction from a first-person view can be improved by
considering the head pose. We compared our method with a conventional Kalman
filter-based approach, and found our method to be more accurate. The experiment
results show that considering the head pose helps improve the accuracy of 3D
head-position prediction.
