Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multiple Collaborative Kernel Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Optimal Kernel Placement for Reliable Visual Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Kernel-based Template Alignment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Toward Optimal Kernel-based Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multiple Collaborative Kernel Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
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Since the multiple kernel representation opened in tracking the possibility of representing several features of the target in the same model, tracking multiple features using kernel-based methods has received a great attention. In spite of these efforts, the formulation has been reduced to tracking planar targets or targets rotating inside a plane parallel to the image plane. The aim of this paper is to extend the multi-kernel tracking to cope with situations different to those. To this end, we consider the triangular mesh described by the centers of the kernels and we develop the estimation of a set of affine transforms, one at each mesh triangle, subject to the constraints that each affine transform of a triangle must be compatible with the affine transforms coming from contiguous triangles. The method is applied to sequences including face and car tracking. Results show an outperformance respect to previous kernel tracking methods, which generally work with a too restricted set of movements.