Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
A primal-dual perspective of online learning algorithms
Machine Learning
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Sequence Labelling SVMs Trained in One Pass
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Weighted random sampling with a reservoir
Information Processing Letters
Robust Visual Tracking and Vehicle Classification via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time visual tracking using compressive sensing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-sparse linear representations for visual tracking with online reservoir metric learning
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Struck: Structured output tracking with kernels
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Graph mode-based contextual kernels for robust SVM tracking
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
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Robust visual tracking requires constant update of the target appearance model, but without losing track of previous appearance information. One of the difficulties with the online learning approach to this problem has been a lack of flexibility in the modelling of the inevitable variations in target and scene appearance over time. The traditional online learning approach to the problem treats each example equally, which leads to previous appearances being forgotten too quickly and a lack of emphasis on the most current observations. Through analysis of the visual tracking problem, we develop instead a novel weighted form of online risk which allows more subtlety in its representation. However, the traditional online learning framework does not accommodate this weighted form. We thus also propose a principled approach to weighted online learning using weighted reservoir sampling and provide a weighted regret bound as a theoretical guarantee of performance. The proposed novel online learning framework can handle examples with different importance weights for binary, multiclass, and even structured output labels in both linear and non-linear kernels. Applying the method to tracking results in an algorithm which is both efficient and accurate even in the presence of severe appearance changes. Experimental results show that the proposed tracker outperforms the current state-of-the-art.