On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Bayesian Filtering for Object 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
Dynamic Appearance Modeling for Human Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
Visual Tracking via Particle Filtering on the Affine Group
International Journal of Robotics Research
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Generalized Kernel-Based Visual Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding window approach is applied to particle filtering by considering a recent set of observations with which internal auxiliary estimates are sequentially calculated, so that the level of uncertainty in the motion model is significantly reduced. With a new auxiliary particle filter, abrupt movements can be effectively handled with a light computational load. Another challenge, severe object appearance change, is adaptively overcome via a modified principal component analysis. By utilizing a recent set of observations, the spatiotemporal piecewise linear subspace of an appearance manifold is incrementally approximated. In addition, distraction in the filtering results is alleviated by using a layered sampling strategy to efficiently determine the best fit particle in the high-dimensional state space. Compared to existing algorithms, the proposed algorithm produces successful results, especially when difficulties are combined.