3D Head-Position Prediction in First-Person View by Considering Head Pose for Human-Robot Eye Contact

TL;DR

This paper proposes a method for predicting 3D head position in first-person view to improve eye contact in human-robot interaction, by incorporating head pose information to enhance prediction accuracy.

Contribution

It introduces a head-position prediction method that considers head pose, outperforming traditional Kalman filter-based approaches in accuracy.

Findings

Considering head pose improves prediction accuracy

The method outperforms Kalman filter-based approaches

Head movement patterns aid in better head-position forecasting

Abstract

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.