Skip to main content
SpringerLink
Log in

Appearance Sharing for Collective Human Pose Estimation

  • Conference paper
Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7724))

Included in the following conference series:

Abstract

While human pose estimation (HPE) techniques usually process each test image independently, in real applications images come in collections containing interdependent images. Often several images have similar backgrounds or show persons wearing similar clothing (foreground). We present a novel human pose estimation technique to exploit these dependencies by sharing appearance models between images. Our technique automatically determines which images in the collection should share appearance. We extend the state-of-the art HPE model of Yang and Ramanan to include our novel appearance sharing cues and demonstrate on the highly challenging Leeds Sports Poses dataset that they lead to better results than traditional single-image pose estimation.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ramanan, D.: Learning to parse images of articulated bodies. In: NIPS (2006)

    Google Scholar 

  2. Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: CVPR (2008)

    Google Scholar 

  3. Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: People detection and articulated pose estimation. In: CVPR (2009)

    Google Scholar 

  4. Eichner, M., Ferrari, V.: Better appearance models for pictorial structures. In: BMVC (2009)

    Google Scholar 

  5. Sapp, B., Jordan, C., Taskar, B.: Adaptive pose priors for pictorial structures. In: CVPR (2010)

    Google Scholar 

  6. Sapp, B., Toshev, A., Taskar, B.: Cascaded Models for Articulated Pose Estimation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 406–420. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human pose estimation. In: BMVC (2010)

    Google Scholar 

  8. Tran, D., Forsyth, D.: Improved Human Parsing with a Full Relational Model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 227–240. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Johnson, S., Everingham, M.: Learning effective human pose estimation from inaccurate annotation. In: CVPR (2011)

    Google Scholar 

  10. Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR (2011)

    Google Scholar 

  11. Andriluka, M., Sigal, L., Black, M.J.: Benchmark Datasets for Pose Estimation and Tracking. Springer (2011)

    Google Scholar 

  12. Kumar, M.P., Torr, P.H.S., Zisserman, A.: Efficient discriminative learning of parts-based models. In: ICCV (2009)

    Google Scholar 

  13. Jiang, H., Martin, D.R.: Global pose estimation using non-tree models. In: CVPR (2008)

    Google Scholar 

  14. Sun, M., Savarese, S.: Articulated part-based model for joint object detection and pose estimation. In: ICCV (2011)

    Google Scholar 

  15. Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. IJCV (2005)

    Google Scholar 

  16. Mori, G., Ren, X., Efros, A., Malik, J.: Recovering human body configurations: Combining segmentation and recognition. In: CVPR (2004)

    Google Scholar 

  17. Jiang, H.: Human pose estimation using consistent max-covering. In: ICCV (2009)

    Google Scholar 

  18. Dalal, N., Triggs, B.: Histogram of Oriented Gradients for Human Detection. In: CVPR (2005)

    Google Scholar 

  19. Bissacco, A., Yang, M.H., Soatto, S.: Fast human pose estimation using appearance and motion via multi-dimensional boosting regression. In: CVPR (2007)

    Google Scholar 

  20. Sapp, B., Weiss, D., Taskar, B.: Parsing human motion with stretchable models. In: CVPR (2011)

    Google Scholar 

  21. Park, D., Ramanan, D.: N-best maximal decoders for part models. In: ICCV (2011)

    Google Scholar 

  22. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. on PAMI (2010)

    Google Scholar 

  23. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley and Sons (2001)

    Google Scholar 

  24. Graham, R.L., Groetschel, M., Lovasz, L.: Handbook of Combinatorics. Elsevier (1995)

    Google Scholar 

  25. Ferrari, V., Tuytelaars, T., Van Gool, L.: Real-time affine region tracking and coplanar grouping. In: CVPR (2001)

    Google Scholar 

  26. Tian, T.-P., Sclaroff, S.: Fast Multi-Aspect 2D Human Detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 453–466. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  27. Singh, V.K., Nevatia, R., Huang, C.: Efficient Inference with Multiple Heterogeneous Part Detectors for Human Pose Estimation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 314–327. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  28. Pishchulin, L., Jain, A., Andriluka, M., Thormaehlen, T., Schiele, B.: Articulated people detection and pose estimation: Reshaping the future. In: CVPR (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ETH Zurich, Switzerland

    Marcin Eichner

  2. University of Edinburgh, United Kingdom

    Vittorio Ferrari

Authors
  1. Marcin Eichner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vittorio Ferrari
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Eichner, M., Ferrari, V. (2013). Appearance Sharing for Collective Human Pose Estimation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37331-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37330-5

  • Online ISBN: 978-3-642-37331-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation