Adaptive Rao-Blackwellized particle filter and its evaluation for tracking in surveillance

Xinyu Xu, Baoxin Li

Research output: Contribution to journalArticle

72 Scopus citations

Abstract

Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter.

Original languageEnglish (US)
Pages (from-to)838-849
Number of pages12
JournalIEEE Transactions on Image Processing
Volume16
Issue number3
DOIs
StatePublished - Mar 1 2007

Keywords

  • Particle filter
  • Rao-Blackwellization
  • Video-based surveillance
  • Visual tracking

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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