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  Human Pose Estimation with Fields of Parts

Kiefel, M., & Gehler, P. (2014). Human Pose Estimation with Fields of Parts. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision - ECCV 2014. Proceedings, Part 5 (pp. 331-346). Cham et al.: Springer International Publishing.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-C6FF-3 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0028-7AE4-C
Genre: Conference Paper

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 Creators:
Kiefel, Martin1, Author              
Gehler, Peter2, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497642              

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Free keywords: Abt. Black; Abt. Schölkopf
 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 5.5 percent in terms of Average Precision of Keypoints (APK).

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Language(s): eng - English
 Dates: 2014-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-319-10602-1_22
BibTex Citekey: kiefel14eccv
 Degree: -

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Title: ECCV 2014 - 13th European Conference on Computer Vision
Place of Event: Zürich
Start-/End Date: 2014-09-06 - 2014-09-12

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Title: Computer Vision - ECCV 2014. Proceedings, Part 5
Source Genre: Proceedings
 Creator(s):
Fleet, David, Editor
Pajdla, Tomas, Editor
Schiele, Bernt, Editor
Tuytelaars, Tinne, Editor
Affiliations:
-
Publ. Info: Cham et al. : Springer International Publishing
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 331 - 346 Identifier: ISBN: 978-3-319-10601-4
ISBN: 978-3-319-10602-1

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Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 8693 Sequence Number: - Start / End Page: - Identifier: -