Efficient particle filtering via sparse kernel density estimation

Amit Banerjee, Philippe Burlina

Research output: Contribution to journalArticlepeer-review


Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to appropriately sample from the posterior distribution, maintain multiple hypotheses, and alleviate computational costs while preserving tracking accuracy. To address these issues, a novel utilization of the support vector data description (SVDD) density estimation method within the particle filtering framework is presented. The SVDD density estimate can be integrated into a wide range of PFs to realize several benefits. It yields a sparse representation of the posterior density that reduces the computational complexity of the PF. The proposed approach also provides an analytical expression for the posterior distribution that can be used to identify its modes for maintaining multiple hypotheses and computing the MAP estimate, and to directly sample from the posterior. We present several experiments that demonstrate the advantages of incorporating a sparse kernel density estimate in a particle filter.

Original languageEnglish (US)
Article number5443441
Pages (from-to)2480-2490
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number9
StatePublished - Sep 1 2010


  • Bayesian filtering
  • machine learning
  • particle filters
  • support vectors
  • tracking

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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