International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Introduction to Monte Carlo methods
Learning in graphical models
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
Learning Dynamics of Complex Motions from Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Learning Dynamical Models Using Expectation-Maximisation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Probabilistic Contour Discriminant for Object Localisation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Information Fusion Framework for Robust Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Performance of Distribution Tracking through Background Mismatch
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Tracking Multiple Mouse Contours (without Too Many Samples)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion driven particle filtering for human body tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dependent Multiple Cue Integration for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A Rao-blackwellized parts-constellation tracker
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Particle Filtering for Large-Dimensional State Spaces With Multimodal Observation Likelihoods
IEEE Transactions on Signal Processing - Part I
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Active contours for tracking distributions
IEEE Transactions on Image Processing
Embedding Motion in Model-Based Stochastic Tracking
IEEE Transactions on Image Processing
Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance
IEEE Transactions on Image Processing
A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior
IEEE Transactions on Image Processing
Robust Shape Tracking With Multiple Models in Ultrasound Images
IEEE Transactions on Image Processing
Shape based appearance model for kernel tracking
Image and Vision Computing
Robust decentralized multi-model adaptive template tracking
Pattern Recognition
Dynamic appearance model for particle filter based visual tracking
Pattern Recognition
Efficient and robust multi-template tracking using multi-start interactive hybrid search
Computer Vision and Image Understanding
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This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.