@inproceedings{679769055f40461cb7954d3947bfecd0,
title = "Sensor fusion for vision-based indoor head pose tracking",
abstract = "Accurate head pose tracking is a key issue for indoor augmented reality systems. This paper proposes a novel approach to track head pose of indoor users using sensor fusion. The proposed approach utilizes a track-to-track fusion framework composed of extended Kalman filters and fusion filter to fuse the poses from the two complementary tracking modes of inside-out tracking (IOT) and outside-in tracking (OIT). A vision-based head tracker is constructed to verify our approach. Primary experimental results show that the tracker is capable of achieving more accurate and stable pose than the single tracking mode of IOT or OIT, which validates the usefulness of the proposed sensor fusion approach.",
keywords = "Augmented reality, Extended Kalman filter, Head pose tracking, Sensor fusion",
author = "Bin Luo and Yongtian Wang and Yue Liu",
year = "2009",
doi = "10.1109/ICIG.2009.145",
language = "English",
isbn = "9780769538839",
series = "Proceedings of the 5th International Conference on Image and Graphics, ICIG 2009",
publisher = "IEEE Computer Society",
pages = "677--682",
booktitle = "Proceedings of the 5th International Conference on Image and Graphics, ICIG 2009",
address = "United States",
note = "5th International Conference on Image and Graphics, ICIG 2009 ; Conference date: 20-09-2009 Through 23-09-2009",
}