Tracking requirements for augmented reality
Communications of the ACM - Special issue on computer augmented environments: back to the real world
Presence: Teleoperators and Virtual Environments
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Marker-less Tracking for AR: A Learning-Based Approach
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
Heteroscedastic errors-in-variables models in computer vision
Heteroscedastic errors-in-variables models in computer vision
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fully Automated and Stable Registration for Augmented Reality Applications
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Real-Time Localisation and Mapping with Wearable Active Vision
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Initialization for Marker-Less Tracking: A Sensor Fusion Approach
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online camera pose estimation in partially known and dynamic scenes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Real-Time Tracking Error Estimation for Augmented Reality for Registration with Linecode Markers
IEICE - Transactions on Information and Systems
A graphical model based solution to the facial feature point tracking problem
Image and Vision Computing
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Estimation of camera pose is an integral part of augmented reality systems. Vision-based methods offer a flexible and accurate method for this estimation. Current vision based methods rely on markers to reduce the computation and increase robustness of the pose estimation. However, this limits the algorithm's applicability while being expensive since the markers also require maintenance. Alternatively, reconstructed scene features can be used for pose estimation but this can lead to a loss of accuracy. To avoid this we propose a two-stage balanced tracking method which does not require any visual markers in the scene. The first stage of our method is based on the sequential recovery of structure from motion which allows the system to learn the scene from a few frames in which the markers are visible. In the next stage, the learned features are used for camera tracking. The system ensures greater accuracy and reduces error drift due to its use of the HEIV estimator which is provably unbiased to the first degree. We also make use of a novel method for the detection and removal of outliers which are unavoidable in such systems. The experiments show the superiority of our method when compared to a nonlinear method based on Levenberg-Marquardt minimization.