The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

Avishy Carmi, François Septier, Simon J. Godsill

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. 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. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios.

Original languageEnglish
Pages (from-to)2454-2467
Number of pages14
Issue number10
StatePublished - 1 Oct 2012
Externally publishedYes


  • Evolutionary MCMC
  • Feature tracking
  • Markov chain Monte Carlo filtering
  • Multiple cluster tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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