Name
Abstract | ||
|---|---|---|
Bayesian target tracking methods consist in filtering successive measurements coming from a detector. Linear and nonlinear Gaussian Bayesian filters are well adapted to estimate the Successive a posteriori,late distributions of a single moving target from a sequence of observations. However, when tracking several targets in a Cluttered environment the previous techniques must be combined with dedicated procedures for validating and associating the measurements to their predictions. Gating validation techniques are used to increase reliability of the association technique by retaining only the measurements that Could be originated from predicted measurements. In standard techniques, the only constrain imposed oil the gate is to contain the correct measurement. However, as the shape of the validation gate is related to the covariance of the transition noise, it is of major importance to estimate it in a reliable manner. In this paper, we therefore review several methods to update the covariance of transition noise and we propose a new one that enables the validation gate to be adapted both to the smoothly evolving dynamic of a moving target and to an abruptly changing dynamic. All the methods are compared for performance on microscopy image sequences which typically contain objects that abruptly change their behaviors. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICIP.2004.1418711 | ICIP |
Keywords | Field | DocType |
Bayes methods,Gaussian processes,covariance analysis,image sequences,nonlinear filters,optical microscopy,optical tracking,target tracking,video signal processing,Gaussian Bayesian multi-target tracking,adaptive gating,association technique reliability,linear Gaussian Bayesian filters,microscopy image sequences,moving target,moving target dynamic,multiple targets,nonlinear Gaussian Bayesian filters,state distribution estimation,transition noise covariance,video microscopy | Computer vision,Nonlinear system,Pattern recognition,Computer science,A priori and a posteriori,Filter (signal processing),Gaussian,Gaussian process,Artificial intelligence,Detector,Covariance,Bayesian probability | Conference |
Volume | ISSN | Citations |
1 | 1522-4880 | 2 |
PageRank | References | Authors |
0.45 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Auguste Genovesio | 1 | 42 | 7.98 |
| Belhassine, Z. | 2 | 2 | 0.45 |
| Olivo-Marin, J.-C. | 3 | 63 | 8.68 |