The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

Avishy Carmi, François Septier, Simon J. Godsill

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

We present a new filtering algorithm for tracking multiple clusters of coordinated targets. Based on a Markov Chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. Following our previous work, we adopt here two strategies for increasing the sampling efficiency of the basic MCMC scheme: an evolutionary stage which allows improved exploration of the sample space, and an EM-based method for making optimized proposals based on the frame likelihood. The algorithm's performance is assessed and demonstrated in both synthetic and real tracking scenarios.

Original languageEnglish
Title of host publication2009 12th International Conference on Information Fusion, FUSION 2009
Pages1179-1186
Number of pages8
StatePublished - 18 Nov 2009
Externally publishedYes
Event2009 12th International Conference on Information Fusion, FUSION 2009 - Seattle, WA, United States
Duration: 6 Jul 20099 Jul 2009

Publication series

Name2009 12th International Conference on Information Fusion, FUSION 2009

Conference

Conference2009 12th International Conference on Information Fusion, FUSION 2009
Country/TerritoryUnited States
CitySeattle, WA
Period6/07/099/07/09

Keywords

  • EM algorithm
  • Evolutionary MCMC
  • Markov chain Monte Carlo filtering
  • Multiple cluster tracking

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