Human Pose Estimation with Fields of Parts

  • Martin Kiefel
  • Peter Vincent Gehler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)

Abstract

This paper proposes a new formulation of the human pose estimation problem. We present the Fields of Parts model, a binary Conditional Random Field model designed to detect human body parts of articulated people in single images.

The Fields of Parts model is inspired by the idea of Pictorial Structures, it models local appearance and joint spatial configuration of the human body. However the underlying graph structure is entirely different. The idea is simple: we model the presence and absence of a body part at every possible position, orientation, and scale in an image with a binary random variable. This results into a vast number of random variables, however, we show that approximate inference in this model is efficient. Moreover we can encode the very same appearance and spatial structure as in Pictorial Structures models.

This approach allows us to combine ideas from segmentation and pose estimation into a single model. The Fields of Parts model can use evidence from the background, include local color information, and it is connected more densely than a kinematic chain structure. On the challenging Leeds Sports Poses dataset we improve over the Pictorial Structures counterpart by 6.0% in terms of Average Precision of Keypoints.

Keywords

Human Pose Estimation Efficient Inference 

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Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Martin Kiefel
    • 1
  • Peter Vincent Gehler
    • 1
  1. 1.Max Planck Institute for Intelligent SystemsTübingenGermany

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