Tracking with a new distribution metric in a particle filtering framework

Romeil Sandhu, Tryphon T Georgiou, Allen Tannenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Tracking involves estimating not only the global motion but also local perturbations or deformations corresponding to a specified object of interest. From this, motion can be decoupled into a finite dimensional state space (the global motion) and the more interesting infinite dimensional state space (deformations). Recently, the incorporation of the particle filter with geometric active contours which use first and second moments has shown robust tracking results. By generalizing the statistical inference to entire probability distributions, we introduce a new distribution metric for tracking that is naturally able to better model the target. Also, due to the multiple hypothesis nature of particle filtering, it can be readily seen that if the background resembles the foreground, then one might lose track. Even though this can be described as a finite dimensional problem where global motion can be modeled and learned online through a filtering process, we approach this task by incorporating a separate energy term in the deformable model that penalizes large centroid displacements. Robust results are obtained and demonstrated on several surveillance sequences.

Original languageEnglish (US)
Title of host publicationImage Processing
Subtitle of host publicationMachine Vision Applications
Volume6813
DOIs
StatePublished - Mar 31 2008
EventImage Processing: Machine Vision Applications - San Jose, CA, United States
Duration: Jan 29 2008Jan 31 2008

Other

OtherImage Processing: Machine Vision Applications
CountryUnited States
CitySan Jose, CA
Period1/29/081/31/08

Keywords

  • Distributions
  • Geometric active contours
  • Particle filtering
  • Tracking, metrics

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