Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Support vector density estimation
Advances in kernel methods
Mode-Finding for Mixtures of Gaussian Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Support Vector Data Description
Machine Learning
Structure from Motion Using Sequential Monte Carlo Methods
International Journal of Computer Vision
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
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Efficient visual tracking using particle filter with incremental likelihood calculation
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to appropriately sample from the posterior distribution, maintain multiple hypotheses, and alleviate computational costs while preserving tracking accuracy. To address these issues, a novel utilization of the support vector data description (SVDD) density estimation method within the particle filtering framework is presented. The SVDD density estimate can be integrated into a wide range of PFs to realize several benefits. It yields a sparse representation of the posterior density that reduces the computational complexity of the PF. The proposed approach also provides an analytical expression for the posterior distribution that can be used to identify its modes for maintaining multiple hypotheses and computing the MAP estimate, and to directly sample from the posterior. We present several experiments that demonstrate the advantages of incorporating a sparse kernel density estimate in a particle filter.