A Robust Circular Fiducial Detection Technique and Real-Time 3D Camera Tracking

Abstract

In this paper a new marker-based approach is presented for 3D camera pose tracking in indoor Augmented Reality (AR). We propose to combine a circular fiducials detection technique with a particle filter to incrementally compute the camera 3D pose parameters. In order to deal with partial occlusions, we have implemented an efficient method for fitting ellipse to scattered data. So even incomplete data will always return an ellipse corresponding to the visible part of the fiducial image. The other advantage of our approach comparing to the related camera pose estimation works is its capacity to naturally discard outliers which occur because of image noises. Results from real data in an augmented reality setup are presented, demonstrating the efficiency and robustness of the proposed method.