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A Memory-Based Particle Filter for Visual Tracking through Occlusions

  • Antonio S. Montemayor
  • Juan José Pantrigo
  • Javier Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

Visual detection and target tracking are interdisciplinary tasks oriented to estimate the state of moving objects in an image sequence. There are different techniques focused on this problem. It is worth highlighting particle filters and Kalman filters as two of the most important tracking algorithms in the literature. In this paper, we presented a visual tracking algorithm which combines the particle filter framework with memory strategies to handle occlusions, called as memory-based particle filter (MbPF). The proposed algorithm follows the classical particle filter stages when a confidence measurement can be obtained from the system. Otherwise, a memory-based module try to estimate the hidden target state and to predict its future states using the process history. Experimental results showed that the performance of the MbPF is better than a standard particle filter when dealing with occlusion situations.

Keywords

Particle Filter Visual Tracking Multiple Object Tracking Standard Particle Sequential Monte Carlo Algorithm 
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 2009

Authors and Affiliations

  • Antonio S. Montemayor
    • 1
  • Juan José Pantrigo
    • 1
  • Javier Hernández
    • 1
  1. 1.Departamento de Ciencias de la ComputaciónUniversidad Rey Juan CarlosMadridSpain

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