The nature of statistical learning theory
The nature of statistical learning theory
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quasi-Random Sampling for Condensation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Weighted and Robust Incremental Method for Subspace Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selecting Discriminative Tracking Features Using Particle Filter
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 Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Density-Based Appearance Modeling for Object Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In order to construct a flexible representation for robust and efficient tracking, a novel real-time tracking method based on online learning is proposed in this paper. Under Bayesian framework, RVM is used to learn the log-likelihood ratio of the statistics of the interested object region to those of the nearby backgrounds. Then, the online selected sparse vectors by RVM are integrated to construct an adaptive representation of the tracked object. Meanwhile, the trained RVM classifier is embedded into particle filtering for tracking. To avoid distraction by the particles in background region, the extreme outlier model is incorporated to describe the posterior probability distribution of all particles. Subsequently, mean-shift clustering and EM algorithm are combined to estimate the posterior state of the tracked object. Experimental results over real-world sequences have shown that the proposed method can efficiently and effectively handle drastic illumination variation, partial occlusion and rapid changes in viewpoint, pose and scale.