Sensor Fusion for Vision-Based Indoor Head Pose Tracking

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.