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

arXiv: https://arxiv.org/abs/2103.06417 · HTML: https://mlsanity.com/paper/2103.06417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Fine-Grained Head Pose Estimation Without Keypoints](https://mlsanity.com/paper/1710.00925/) (2017)
- [Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty](https://mlsanity.com/paper/1711.09026/) (2017)
- [Future Person Localization in First-Person Videos](https://mlsanity.com/paper/1711.11217/) (2017)
- [MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses](https://mlsanity.com/paper/1805.00652/) (2018)

## Built on by (most cited first, 1 of 1)

- [Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation](https://mlsanity.com/paper/2404.17031/) (2024)

## Abstract (arXiv)

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.