Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Focus of Attention for Robust Vision-Based Tracking
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projective registration with difference decomposition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Autocalibration and the absolute quadric
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Video Mosaics for Virtual Environments
IEEE Computer Graphics and Applications
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A comparison of viewing geometries for augmented reality
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Computer Vision and Image Understanding
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In registration-based motion tracking precise pose between a reference template and the current image is determined by warping image patches into the template coordinates and matching pixel-wise intensities. Efficient such algorithms are based on relating spatial and temporal derivatives using numerical optimization algorithms. We extend this approach from planar patches into a formulation where the 3D geometry of a scene is both estimated from uncalibrated video and used in the tracking of the same video sequence. Experimentally we compare convergence and accuracy of our uncalibrated 3D tracking to previous approaches. Notably, the 3D algorithm can successfully track over significantly larger pose changes than ones using only planar regions. It also detects occlusions and removes/inserts tracking regions as appropriate in response.