Robust circular fiducials tracking and camera pose estimation using particle filtering

This paper presents a real-time circular targets tracking approach for camera pose estimation which is based on particle filtering framework. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model. Their ability to deal with non-linearities and non-Gaussian statistics allows to improve robustness comparing to existing approaches, such as those based on the Kalman filter. We propose to combine a circular fiducials detection algorithm with a particle filter to compute the camera 3D pose parameters. One of the main advantages of our approach comparing to the related camera pose estimation works is its capacity to naturally discard outliers which occur because of either image noise or occlusion. Results from real data in an augmented reality setup are then presented, demonstrating the efficiency and robustness of the proposed method.