The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Proceedings of the 1998 conference on Advances in neural information processing systems II
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Towards Improved Observation Models for Visual Tracking: Selective Adaptation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Hyperdynamics Importance Sampling
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Dynamical Models Using Expectation-Maximisation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
A Stochastic Filter for Fluid Motion Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Unified point selection and surface-based registration using a particle filter
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Conditional filters for image sequence-based tracking - application to point tracking
IEEE Transactions on Image Processing
Mean field approach for tracking similar objects
Computer Vision and Image Understanding
Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
Tracking Closed Curves with Non-linear Stochastic Filters
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A variant of particle filtering using historic datasets for tracking complex geospatial phenomena
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Tracking level set representation driven by a stochastic dynamics
Proceedings of the 7th international conference on Curves and Surfaces
A novel particle filter with implicit dynamic model for irregular motion tracking
Machine Vision and Applications
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The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the state-space exploration, which may be inefficient due to a crude choice of the function used to sample in the associated probability space. A careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear Gaussian models. Such models are characterized by a non linear dynamic equation, a linear measurement equation and additive Gaussian noises. They allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter, and enable building a relevant approximation of a validation gate. Despite of these potential advantages partial linear Gaussian models have not been investigated. The aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions, ambiguities due to cluttered backgrounds and large state space. Three instances of these models are proposed. After a theoretical analysis, their significance is demonstrated by their performance for tracking points and planar objects in challenging real-world image sequences.