Team for Research in
Ubiquitous Secure Technology

Citation
Emily B. Fox, David Choi, Alan S. Willsky. "Nonparametric Bayesian Methods for Large Scale Multi-Target Tracking". Proceedings of the 40th Asilomar Conference on Signals, Systems, and Computers, January, 2006.

Abstract
We consider the problem of data association for multi-target tracking in the presence of an unknown number of targets. For this application, inference in models which place parametric priors on large numbers of targets becomes computationally intractable. As an alternative to parametric models, we explore the utility of nonparametric Bayesian methods, specifically Dirichlet processes, which allow us to put a flexible, data-driven prior on the number of targets present in our observations. Dirichlet processes provide a prior on partitions of the observations among targets whose dynamics are individually described by state space models. These partitions represent the tracks with which the observations are associated. We provide preliminary data association results for the implementation of Dirichlet processes in this scenario.

Electronic downloads

• Asilomar.pdf · application/pdf · 2735 kbytes

• HTML

  Emily B. Fox, David Choi, Alan S. Willsky. <a
  href="http://www.truststc.org/pubs/261.html"
  >Nonparametric Bayesian Methods for Large Scale
  Multi-Target Tracking</a>, Proceedings of the 40th
  Asilomar Conference on Signals, Systems, and Computers,
  January, 2006.

• Plain text

  Emily B. Fox, David Choi, Alan S. Willsky.
  "Nonparametric Bayesian Methods for Large Scale
  Multi-Target Tracking". Proceedings of the 40th
  Asilomar Conference on Signals, Systems, and Computers,
  January, 2006.

• BibTeX

  @inproceedings{FoxChoiWillsky06_NonparametricBayesianMethodsForLargeScaleMultiTarget,
      author = {Emily B. Fox and David Choi and Alan S. Willsky},
      title = {Nonparametric Bayesian Methods for Large Scale
                Multi-Target Tracking},
      booktitle = {Proceedings of the 40th Asilomar Conference on
                Signals, Systems, and Computers},
      month = {January},
      year = {2006},
      abstract = {We consider the problem of data association for
                multi-target tracking in the presence of an
                unknown number of targets. For this application,
                inference in models which place parametric priors
                on large numbers of targets becomes
                computationally intractable. As an alternative to
                parametric models, we explore the utility of
                nonparametric Bayesian methods, specifically
                Dirichlet processes, which allow us to put a
                flexible, data-driven prior on the number of
                targets present in our observations. Dirichlet
                processes provide a prior on partitions of the
                observations among targets whose dynamics are
                individually described by state space models.
                These partitions represent the tracks with which
                the observations are associated. We provide
                preliminary data association results for the
                implementation of Dirichlet processes in this
                scenario.},
      URL = {http://www.truststc.org/pubs/261.html}
  }
  

Posted by Emily B. Fox on 15 Jul 2007.
Groups: trust
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