Efficient particle filtering using RANSAC with application to 3D face tracking

Le Lu, Xiangtian Dai, Gregory Hager

Research output: Contribution to journalArticle

Abstract

Particle filtering is a very popular technique for sequential state estimation. However, in high-dimensional cases where the state dynamics are complex or poorly modeled, thousands of particles are usually required for real applications. This paper presents a hybrid sampling solution that combines RANSAC and particle filtering. In this approach, RANSAC provides proposal particles that, with high probability, represent the observation likelihood. Both conditionally independent RANSAC sampling and boosting-like conditionally dependent RANSAC sampling are explored. We show that the use of RANSAC-guided sampling reduces the necessary number of particles to dozens for a full 3D tracking problem. This method is particularly advantageous when state dynamics are poorly modeled. We show empirically that the sampling efficiency (in terms of likelihood) is much higher with the use of RANSAC. The algorithm has been applied to the problem of 3D face pose tracking with changing expression. We demonstrate the validity of our approach with several video sequences acquired in an unstructured environment.

Original languageEnglish (US)
Pages (from-to)581-592
Number of pages12
JournalImage and Vision Computing
Volume24
Issue number6
DOIs
StatePublished - Jun 1 2006

Keywords

  • Particle filtering
  • RANSAC
  • Random projection
  • Robust 3D face tracking

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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