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Markerless Multi-view Articulated Pose Estimation Using Adaptive Hierarchical Particle Swarm Optimisation

  • Spela Ivekovic
  • Vijay John
  • Emanuele Trucco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

In this paper, we present a new adaptive approach to multi-view markerless articulated human body pose estimation from multi-view video sequences, using Particle Swarm Optimisation (PSO). We address the computational complexity of the recently developed hierarchical PSO (HPSO) approach, which successfully estimated a wide range of different motion with a fixed set of parameters, but incurred an unnecessary overhead in computational complexity. Our adaptive approach, called APSO, preserves the black-box property of the HPSO in that it requires no parameter value input from the user. Instead, it adaptively changes the value of the search parameters online, depending on the quality of the pose estimate in the preceding frame of the sequence. We experimentally compare our adaptive approach with HPSO on four different video sequences and show that the computational complexity can be reduced without sacrificing accuracy and without requiring any user input or prior knowledge about the estimated motion type.

Keywords

Particle Swarm Optimisation Algorithm Motion Capture Inertia Weight Iterative Close Point Preceding Frame 
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 2010

Authors and Affiliations

  • Spela Ivekovic
    • 1
  • Vijay John
    • 1
  • Emanuele Trucco
    • 1
  1. 1.School of ComputingUniversity of DundeeDundee

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