IEEE Transactions on Pattern Analysis and Machine Intelligence
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Modeling Appearance Change in Image Sequences
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive weighting of local classifiers by particle filters for robust tracking
Pattern Recognition
An incremental Bhattacharyya dissimilarity measure for particle filtering
Pattern Recognition
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Game-theoretical occlusion handling for multi-target visual tracking
Pattern Recognition
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Generative subspace models like probabilistic principal component analysis (PCA) have been shown to be quite effective for visual tracking problems due to their representational power that can capture the generation process for high-dimensional image data. The recent advance of incremental learning has further enabled them to be practical for real-time scenarios. Despite these benefits, the PCA-based approaches in visual tracking can be potentially susceptible to noise such as partial occlusion due to their compatibility judgement based on the goodness of fitting for the entire image patch. In this paper we introduce a novel appearance model that measures the goodness of target matching as the correlation score between partial sub-patches within a target. We incorporate the canonical correlation analysis (CCA) into the probabilistic filtering framework in a principled manner, and derive how the correlation score can be evaluated efficiently in the proposed model. We then provide an efficient incremental learning algorithm that updates the CCA subspaces to adapt to new data available from the previous tracking results. We demonstrate the significant improvement in tracking accuracy achieved by the proposed approach on extensive datasets including the large-scale real-world YouTube celebrity video database as well as the novel video lecture dataset acquired from British Machine Vision Conference held in 2009, where both datasets are challenging due to the abrupt changes in pose, size, and illumination conditions.