Tracking and data association
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning in graphical models
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The Journal of Machine Learning Research
Variational Maximum A Posteriori by Annealed Mean Field Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Interactively Distributed Multi-Object Tracking Using a Magnetic-Inertia Potential Model
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Cell population tracking and lineage construction with spatiotemporal context
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Occlusion management in sequential mean field Monte Carlo methods
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Recognizing facial expressions using a novel shape motion descriptor
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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In this paper, we consider the problem of tracking similar objects. We show how a mean field approach can be used to deal with interacting targets and we compare it with Markov Chain Monte Carlo (MCMC). Two mean field implementations are presented. The first one is more general and uses particle filtering. We discuss some simplifications of the base algorithm that reduce the computation time. The second one is based on suitable Gaussian approximations of probability densities that lead to a set of self-consistent equations for the means and covariances. These equations give the Kalman solution if there is no interaction. Experiments have been performed on two kinds of sequences. The first kind is composed of a single long sequence of twenty roaming ants and was previously analysed using MCMC. In this case, our mean field algorithms obtain substantially better results. The second kind corresponds to selected sequences of a football match in which the interaction avoids tracker coalescence in situations where independent trackers fail.