Multi-view Pose Estimation with Flexible Mixtures-of-Parts

  • Emre Dogan
  • Gonen Eren
  • Christian Wolf
  • Eric Lombardi
  • Atilla Baskurt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10617)

Abstract

We propose a new method for human pose estimation which leverages information from multiple views to impose a strong prior on the articulated pose. The novelty of the method concerns the types of coherence modeled. Consistency is maximized over the different views through different terms modeling classical geometric information (coherence of the resulting poses) as well as appearance information which is modeled as latent variables in the global energy function. Experiments on the HumanEva dataset show that the proposed method significantly decreases the estimation error compared to single-view results and attains a 3D PCP score of 86%.

Keywords

Pose estimation Multiple-view 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Emre Dogan
    • 1
    • 2
  • Gonen Eren
    • 2
  • Christian Wolf
    • 1
  • Eric Lombardi
    • 1
  • Atilla Baskurt
    • 1
  1. 1.Université de Lyon, INSA-Lyon, LIRISLyonFrance
  2. 2.Department of Computer EngineeringGalatasaray UniversityIstanbulTurkey

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