Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Track: Conceptual Manifold Map for Closed-Form Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Tracking Non-Stationary Appearances and Dynamic Feature Selection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Tracking Using Foreground-Background Texture Discrimination
International Journal of Computer Vision
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Discriminative nonorthogonal binary subspace tracking
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough-based tracking of non-rigid objects
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Struck: Structured output tracking with kernels
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Object tracking using learned feature manifolds
Computer Vision and Image Understanding
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Visual tracking is a challenging problem, as the appearance of an object may change due to viewpoint variations, illumination changes, and occlusion. It may also leave the field of view (FOV), then reappears. In order to track and reacquire an unknown object with limited labeling data, we propose to learn these changes online and incrementally build a model that encodes all appearance variations while tracking. To address this semi-supervised learning problem, we propose a co-training framework with cascade particle filter to label incoming data continuously and online update hybrid generative and discriminative models. Each of the layers in the cascade contains one or more either generative or discriminative appearance models. The cascade manner of organizing the particle filter enables the efficient evaluation of multiple appearance models with different computational costs; thus improves the speed of the tracker. The proposed online framework provides temporally local tracking that adapts to appearance changes. Moreover, it provides an object-specific detection ability that allows to reacquire an object after total occlusion. Extensive experiments demonstrate that under challenging situations, our method has strong reacquisition ability and robustness to distracters in clutter background. We also provide quantitative comparisons to other state of the art trackers.