Self Adaptive Particle Filter. Soto, A. In *Proceedings of International Join Conference on Artificial Intelligence (IJCAI)*, pages 1398-1406, 2005. Paper abstract bibtex 3 downloads The particle filter has emerged as a useful tool for problems requiring dynamic state estimation. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate these particles at each iteration. Both features are specified beforehand and are kept fixed in the reg- ular implementation of the filter. In practice this may be highly inappropriate since it ignores errors in the models and the varying dynamics of the pro- cesses. This work presents a self adaptive version of the particle filter that uses statistical methods to adapt the number of particles and the propagation function at each iteration. Furthermore, our method presents similar computational load than the stan- dard particle filter. We show the advantages of the self adaptive filter by applying it to a synthetic ex- ample and to the visual tracking of targets in a real video sequence.

@InProceedings{ soto:2005,
author = {A. Soto},
title = {Self Adaptive Particle Filter},
booktitle = {Proceedings of International Join Conference on Artificial
Intelligence (IJCAI)},
pages = {1398-1406},
year = {2005},
abstract = {The particle filter has emerged as a useful tool for
problems requiring dynamic state estimation. The efficiency
and accuracy of the filter depend mostly on the number of
particles used in the estimation and on the propagation
function used to re-allocate these particles at each
iteration. Both features are specified beforehand and are
kept fixed in the reg- ular implementation of the filter.
In practice this may be highly inappropriate since it
ignores errors in the models and the varying dynamics of
the pro- cesses. This work presents a self adaptive version
of the particle filter that uses statistical methods to
adapt the number of particles and the propagation function
at each iteration. Furthermore, our method presents similar
computational load than the stan- dard particle filter. We
show the advantages of the self adaptive filter by applying
it to a synthetic ex- ample and to the visual tracking of
targets in a real video sequence. },
url = {Soto-IJCAI-0 5.pdf}
}

Downloads: 3

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