Skip to main content
SpringerLink
Log in

Human Pose Estimation

  • Reference work entry
  • First Online:
Computer Vision

Synonyms

Articulated pose estimation; Body configuration recovery

Related Concepts

Human Pose Estimation

Definition

Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image.

Background

Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.

Despite many years of research, however, pose estimation remains a very difficult and still largely unsolved problem. Among the most significant challenges are the...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 649.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 899.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agarwal A, Triggs B (2006) Recovering 3d human pose from monocular images. IEEE Trans Pattern Anal Mach Intell 28(1):44–58

    Article  Google Scholar 

  2. Andriluka M, Roth S, Schiele B (2009) Pictorial structures revisited: people detection and articulated pose estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), Miami

    Google Scholar 

  3. Andriluka M, Roth S, Schiele B (2010) Monocular 3d pose estimation and tracking by detection. In: IEEE conference on computer vision and pattern recognition (CVPR), San Francisco

    Google Scholar 

  4. Bergtholdt M, Kappes J, Schmidt S, Schnorr C (2010) A study of parts-based object class detection using complete graphs. Int J Comput Vis 87(1–2):93–117

    Article  MathSciNet  Google Scholar 

  5. Bo L, Sminchisescu C (2010) Twin gaussian processes for structured prediction. Int J Comput Vis 87(1–2):28–52

    Article  Google Scholar 

  6. Bo L, Sminchisescu C, Kanaujia A, Metaxas D (2008) Fast algorithms for large scale conditional 3d prediction. In: IEEE conference on computer vision and pattern recognition (CVPR), Anchorage

    Google Scholar 

  7. Eichner M, Ferrari V (2010) We are family: joint pose estimation of multiple persons. In: European conference on computer vision (ECCV), Heraklion

    Google Scholar 

  8. Felzenszwalb PF, Huttenlocher DP (2005) Pictorial structures for object recognition. Int J Comput Vis 61(1): 55–79

    Article  Google Scholar 

  9. Ferrari V, Marn-Jimnez MJ, Zisserman A (2008) Progressive search space reduction for human pose estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), Anchorage

    Google Scholar 

  10. Gall J, Rosenhahn B, Brox T, Seidel H-P (2010) Optimization and filtering for human motion capture. Int J Comput Vis 87(1–2):75–92

    Article  Google Scholar 

  11. Jiang H (2009) Human pose estimation using consistent max-covering. In: IEEE international conference on computer vision, Kyoto

    Book  Google Scholar 

  12. Kanaujia A, Sminchisescu C, Metaxas D (2007) Semi-supervised hierarchical models for 3d human pose reconstruction. In: IEEE conference on computer vision and pattern recognition (CVPR), Minneapolis

    Google Scholar 

  13. Lee MW, Cohen I (2004) Proposal maps driven mcmc for estimating human body pose in static images. In: IEEE conference on computer vision and pattern recognition (CVPR), Washington, DC

    Google Scholar 

  14. Mori G, Ren X, Efros A, Malik J (2004) Recovering human body configurations: Combining segmentation and recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), Washington, DC

    Google Scholar 

  15. Navaratnam R, Fitzgibbon A, Cipolla R (2007) The joint manifold model for semi-supervised multi-valued regression. In: IEEE international conference on computer vision, Rio de Janeiro

    Book  Google Scholar 

  16. Ramanan D (2006) Learning to parse images of articulated bodies. In: Neural information and processing systems, Vancouver

    Google Scholar 

  17. Ren X, Berg AC, Malik J (2005) Recovering human body configurations using pair-wise constraints between parts. In: International conference on computer vision, Beijing

    Google Scholar 

  18. Shakhnarovich G, Viola P, Darrell T (2003) Fast pose estimation with parameter sensitive hashing. In: International conference on computer vision, Nice

    Book  Google Scholar 

  19. Sigal L, Balan A, Black MJ (2007) Combined discriminative and generative articulated pose and non-rigid shape estimation. In: Neural information and processing systems, Vancouver

    Google Scholar 

  20. Sigal L, Black MJ (2006) Measure locally, reason globally: occlusion-sensitive articulated pose estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), New York

    Google Scholar 

  21. Sigal L, Isard M, Sigelman BH, Black MJ (2003) Attractive people: Assembling loose-limbed models using non-parametric belief propagation. In: Advances in neural information processing systems, Vancouver

    Google Scholar 

  22. Sigal L, Memisevic R, Fleet DJ (2009) Shared kernel information embedding for discriminative inference. In: IEEE conference on computer vision and pattern recognition, Miami

    Book  Google Scholar 

  23. Singh VK, Nevatia R, Huang C (2010) Efficient inference with multiple heterogeneous part detectors for human pose estimation. In: European conference on computer vision (ECCV), Heraklion, pp 314–327

    Google Scholar 

  24. Tian T-P, Sclaroff S (2010) Fast globally optimal 2d human detection with loopy graph models. In: IEEE conference on computer vision and pattern recognition (CVPR), San Francisco

    Google Scholar 

  25. Urtasun R, Darrell T (2008) Sparse probabilistic regression for activity-independent human pose inference. In: IEEE conference on computer vision and pattern recognition (CVPR), Anchorage

    Google Scholar 

  26. Yang Y, Ramanan D (2011) Articulated pose estimation with flexible mixture-of-parts. In: IEEE conference on computer vision and pattern recognition (CVPR), Colorado Springs

    Google Scholar 

  27. Zhang J, Luo J, Collins R, Liu Y (2006) Body localization in still images using hierarchical models and hybrid search. In: IEEE conference on computer vision and pattern recognition (CVPR), New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Disney Research, Pittsburgh, PA, USA

    Leonid Sigal

Authors
  1. Leonid Sigal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Katsushi Ikeuchi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Sigal, L. (2014). Human Pose Estimation. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_584

Download citation

Publish with us

Policies and ethics

Search

Navigation