CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Probabilistic Object Tracking Using Multiple Features
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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Multiple object tracking is a main research area in the computer vision field. Particle filters have shown their performance as a powerful tool allowing to track visual objects giving temporal coherence to incoming observations, as well as offering an excellent framework for this task due to its inherent multimodality. However, traditional algorithms for particle filters do not cope directly with multiple objects and several considerations have to be addressed. In this work, an efficient reclustering strategy is proposed, which takes into account new measurements according to a novelty function, and provides a criterium to determine the minimum required number of particles to be drawn for each tracked object. To show its performance, this strategy has been used as a multiple 2D object tracking for video-surveillance applications. Excellent results are obtained, in terms of efficiency and accuracy.