a Probabilistic Framework for Combining Tracking Algorithms
.
A Probabilistic Framework for Combining Tracking Algorithms.
In CVPR (2), 445-451, 2004
Online Version
A pdf version is available for download.
Abstract
For the past few years researches have been investigating enhancing tracking performance by combining several different tracking algorithms. We propose an analytically justified, probabilistic framework to combine multiple tracking algorithms. The separate tracking algorithms considered output a probability distribution function of the tracked state, sequentially for each image. The algorithms may output either an explicit probability distribution function, or a sample-set of it via CONDENSATION. The proposed framework is general and allows the combination of any set of separate tracking algorithms of this kind, even on different state spaces of different dimensionality, under a few reasonable assumptions. In many of the investigated settings, our approach allows us to treat the separate tracking algorithms as “closed boxes”. In other words, only the state distributions in the input and output are needed for the combination process. The suggested framework was successfully tested using various state spaces and datasets.
Co-authors
Bibtex Entry
@inproceedings{LeichterLR04i,
title = {A Probabilistic Framework for Combining Tracking Algorithms.},
author = {Ido Leichter and Michael Lindenbaum and Ehud Rivlin},
year = {2004},
booktitle = {CVPR (2)},
pages = {445-451},
abstract = {For the past few years researches have been investigating enhancing tracking performance by combining several different tracking algorithms. We propose an analytically justified, probabilistic framework to combine multiple tracking algorithms. The separate tracking algorithms considered output a probability distribution function of the tracked state, sequentially for each image. The algorithms may output either an explicit probability distribution function, or a sample-set of it via CONDENSATION. The proposed framework is general and allows the combination of any set of separate tracking algorithms of this kind, even on different state spaces of different dimensionality, under a few reasonable assumptions. In many of the investigated settings, our approach allows us to treat the separate tracking algorithms as “closed boxes”. In other words, only the state distributions in the input and output are needed for the combination process. The suggested framework was successfully tested using various state spaces and datasets.}
}