Robust Line Tracking Using a Particle Filter For Camera Pose Estimation
Résumé
This paper presents a robust line 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 use the particle filter to compute the posterior density for the camera 3D motion parameters. The experimental results indicate the effectiveness of our approach and demonstrate its robustness even when dealing with severe occlusion.