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International Journal of Computer Vision

, Volume 74, Issue 1, pp 75–102 | Cite as

Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods

  • Elise ArnaudEmail author
  • Etienne Mémin
Article

Abstract

The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the state-space exploration, which may be inefficient due to a crude choice of the function used to sample in the associated probability space. A careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear Gaussian models. Such models are characterized by a non linear dynamic equation, a linear measurement equation and additive Gaussian noises. They allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter, and enable building a relevant approximation of a validation gate. Despite of these potential advantages partial linear Gaussian models have not been investigated. The aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions, ambiguities due to cluttered backgrounds and large state space. Three instances of these models are proposed. After a theoretical analysis, their significance is demonstrated by their performance for tracking points and planar objects in challenging real-world image sequences.

Keywords

sequential Monte Carlo methods optimal importance function Rao-Blackwellization validation gate point tracking planar structure tracking 

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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.Inria Rhône-Alpes/Universite Joseph FourierMontbonnotFrance
  2. 2.IRISA/Univ. Rennes IRennes CedexFrance

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