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Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking

  • Elise Arnaud
  • Etienne Mémin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3023)

Abstract

In this paper, we propose a particle filtering approach for tracking applications in image sequences. The system we propose combines a measurement equation and a dynamic equation which both depend on the image sequence. Taking into account several possible observations, the likelihood is modeled as a linear combination of Gaussian laws. Such a model allows inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter. It also enables building a relevant approximation of a validation gate. We demonstrate the significance of this model for a point tracking application.

Keywords

Image Sequence Point Tracking Cluttered Background Importance Function Gaussian System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Elise Arnaud
    • 1
  • Etienne Mémin
    • 1
  1. 1.IRISAUniversité de Rennes 1Rennes CedexFrance

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