Object pose from 2-D to 3-D point and line correspondences
International Journal of Computer Vision
Superior augmented reality registration by integrating landmark tracking and magnetic tracking
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
SCAAT: incremental tracking with incomplete information
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Vision-Based Pose Computation: Robust and Accurate Augmented Reality Tracking
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Extendible Tracking by Line Auto-Calibration
ISAR '01 Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR'01)
A real-time tracker for markerless augmented reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Markov chain Monte Carlo data association for target tracking
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
A hand-held 3D display system that facilitates direct manipulation of 3D virtual objects
Proceedings of the 8th International Conference on Virtual Reality Continuum and its Applications in Industry
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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.