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Path Relinking Particle Filter for Human Body Pose Estimation

  • Juan José Pantrigo
  • Ángel Sánchez
  • Kostas Gianikellis
  • Abraham Duarte
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

This paper introduces the Path Relinking Particle Filter (PRPF) algorithm for improving estimation problems in human motion capture. PRPF hybridizes both Particle Filter and Path Relinking frameworks. The proposed algorithm increases the performance of general Particle Filter by improving the quality of the estimate, by adapting computational load to problem constraints and by reducing the number of required evaluations of the weighting function. We have applied the PRPF algorithm to 2D human pose estimation. Experimental results show that PRPF drastically reduces the MSE value to obtain the set of markers with respect to Condensation and Sampling Importance Resampling (SIR) algorithms.

Keywords

Mean Square Error Particle Filter Particle Weight Path Relinking Manual Digitize 
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

  • Juan José Pantrigo
    • 1
  • Ángel Sánchez
    • 1
  • Kostas Gianikellis
    • 2
  • Abraham Duarte
    • 1
  1. 1.Universidad Rey Juan CarlosMóstolesSpain
  2. 2.Universidad de ExtremaduraCáceresSpain

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