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
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
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Robust Fragments-based Tracking using the Integral Histogram
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 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Euclidean projections in linear time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On-line ensemble SVM for robust object tracking
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)
Pattern Recognition
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust visual tracking with structured sparse representation appearance model
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
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In this paper, we propose a robust tracking algorithm to handle drifting problem. This algorithm consists of two parts: the first part is the G&D part that combines Generative model and Discriminative model for tracking, and the second part is the View-Based model for target appearance that corrects the result of the G&D part if necessary. In G&D part, we use the Maximum Margin Projection (MMP) to construct a graph model to preserve both local geometrical and discriminant structures of the data manifold in low dimensions. Therefore, such discriminative subspace combined with traditional generative subspace can benefit from both models. In addition, we address the problem of learning maximum margin projection under the Spectral Regression (SR) which results in significant savings in computational time. To further solve the drift, an online learned sparsely represented view-based model of the target is complementary to the G&D part. When the result of G&D part is unreliable, the view-based model can rectify the result in order to avoid drifting. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.