IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Real Time Tracking of 3D Objects
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Scene Modelling, Recognition and Tracking with Invariant Image Features
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Sparse Bayesian Learning for Efficient Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
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
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Tracking as Linear Program on Weak Binary Classifiers
Proceedings of the 30th DAGM symposium on Pattern Recognition
Anytime learning for the NoSLLiP tracker
Image and Vision Computing
Linear Regression and Adaptive Appearance Models for Fast Simultaneous Modelling and Tracking
International Journal of Computer Vision
Online learning of linear predictors for real-time tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Efficient learning of linear predictors using dimensionality reduction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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A novel object representation for tracking is proposed. The tracked object is represented as a constellation of spatially localised linear predictors which are learned on a single training image. In the learning stage, sets of pixels whose intensities allow for optimal least square predictions of the transformations are selected as a support of the linear predictor. The approach comprises three contributions: learning object specific linear predictors, explicitly dealing with the predictor precision – computational complexity trade-off and selecting a view-specific set of predictors suitable for global object motion estimate. Robustness to occlusion is achieved by RANSAC procedure. The learned tracker is very efficient, achieving frame rate generally higher than 30 frames per second despite the Matlab implementation.