Skip to main content
SpringerLink
Log in

Recognition of Actions

  • Chapter
Computer Vision Using Local Binary Patterns

Part of the book series: Computational Imaging and Vision ((CIVI,volume 40))

  • 2182 Accesses

Abstract

Methods for analyzing humans and their actions from monocular or multi-view video data are required in many different applications. In this chapter simple LBP-based approaches for action recognition are introduced. The methods perform very favorably compared to the state-of-the-art for test video sequences commonly used in the research community.

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 EPUB and 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
Hardcover Book
USD 54.99
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. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: Proc. International Conference on Computer Vision, vol. 2, pp. 1395–1402 (2005)

    Google Scholar 

  2. Bobick, A., Davis, J.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)

    Article  Google Scholar 

  3. Boiman, O., Irani, M.: Similarity by composition. In: Proc. Neural Information Processing Systems Conference, p. 8 (2006)

    Google Scholar 

  4. Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: Proc. International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72 (2005)

    Chapter  Google Scholar 

  5. Gavrila, D.M.: The visual analysis of human movement: A survey. Comput. Vis. Image Underst. 73(3), 82–98 (1999)

    Article  MATH  Google Scholar 

  6. Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)

    Article  Google Scholar 

  7. Ikizler, N., Duygulu, P.: Human action recognition using distribution of oriented rectangular patches. In: Proc. ICCV Workshop on Human Motion Understanding, Modeling, Capture and Animation, pp. 271–284 (2007)

    Chapter  Google Scholar 

  8. Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: Proc. International Conference on Computer Vision, pp. 165–173 (2005)

    Google Scholar 

  9. Ke, Y., Sukthankar, R., Hebert, M.: Spatio-temporal shape and flow correlation for action recognition. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  10. Kellokumpu, V., Zhao, G., Pietikäinen, M.: Human activity recognition using a dynamic texture based method. In: Proc. British Machine Vision Conference (2008)

    Google Scholar 

  11. Kellokumpu, V., Zhao, G., Pietikäinen, M.: Texture based description of movements for activity analysis. In: Proc. International Conference on Computer Vision Theory and Applications, vol. 1, pp. 206–213 (2008)

    Google Scholar 

  12. Kellokumpu, V., Zhao, G., Pietikäinen, M.: Dynamic texture based gait recognition. In: Advances in Biometrics. Lecture Notes in Computer Science, vol. 5558, pp. 1000–1009. Springer, Berlin (2009)

    Chapter  Google Scholar 

  13. Kellokumpu, V., Zhao, G., Pietikäinen, M.: Recognition of human actions using texture descriptors. Machine Vision and Applications (2011). doi:10.1007/s00138-009-0233-8

    Google Scholar 

  14. Kim, K., Wong, S., Cipolla, R.: Tensor canonical correlation analysis for action classification. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  15. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.S.: Background modeling and subtraction by codebook construction. In: Proc. IEEE International Conference on Image Processing, pp. 3061–3064 (2004)

    Google Scholar 

  16. Kobyashi, T., Otsu, N.: Action and simultaneous multiple-person identification using cubic higher-order auto-correlation. In: Proc. International Conference on Pattern Recognition, vol. 4, pp. 741–744 (2004)

    Google Scholar 

  17. Laptev, I., Lindeberg, T.: Space-time interest points. In: Proc. International Conference on Computer Vision, vol. 1, pp. 432–439 (2003)

    Chapter  Google Scholar 

  18. Ma, Y., Cisar, P.: Event detection using local binary pattern based dynamic textures. In: Proc. CVPR Workshop on International Conference on Spoken Language Proceedings, pp. 38–44 (2009)

    Google Scholar 

  19. Mattivi, R., Shao, L.: Human action recognition using LBP-TOP as sparse spatio-temporal feature descriptor. In: Proc. International Conference on Computer Analysis of Images and Patterns, pp. 740–747 (2009)

    Chapter  Google Scholar 

  20. Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104, 90–126 (2006)

    Article  Google Scholar 

  21. Niebles, J.C., Fei-Fei, L.: A hierarchical model of shape and appearance for human action classification. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  22. Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 79(3), 299–318 (2008)

    Article  Google Scholar 

  23. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–285 (1989)

    Article  Google Scholar 

  24. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local SVM approach. In: Proc. International Conference on Pattern Recognition, pp. 32–36 (2004)

    Google Scholar 

  25. Scovanner, P., Ali, S., Shah, M.: A 3-dimensional SIFT descriptor and its application to action recognition. In: Proc. ACM Multimedia, pp. 357–360 (2007)

    Google Scholar 

  26. Shao, L., Mattivi, R.: Feature detector and descriptor evaluation in human action recognition. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 477–484 (2010)

    Chapter  Google Scholar 

  27. Shechtman, E., Irani, M.: Space-time behavior based correlation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 405–412 (2005)

    Google Scholar 

  28. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proc. IEEE Conference Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)

    Google Scholar 

  29. Wang, L., Suter, D.: Recognizing human activities from silhouettes: Motion subspace and factorial discriminative graphical model. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  30. Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Comput. Vis. Image Underst. 104(2–3), 249–257 (2006)

    Article  Google Scholar 

  31. Wong, S., Kim, T., Cipolla, R.: Learning motion categories using both semantic and structural information. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6 (2007)

    Google Scholar 

  32. Yeffet, L., Wolf, L.: Local trinary patterns for human action recognition. In: Proc. International Conference on Computer Vision, pp. 492–497 (2009)

    Google Scholar 

  33. Yilmaz, A., Shah, M.: Action sketch: A novel action representation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 984–989 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Vision Group, Department of Computer Science and Engineering, University of Oulu, PO Box 4500, 90014, Oulu, Finland

    Matti Pietikäinen, Abdenour Hadid & Guoying Zhao

  2. Nokia Research Center, Palo Alto, CA, USA

    Timo Ahonen

Authors
  1. Matti Pietikäinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdenour Hadid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoying Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Timo Ahonen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matti Pietikäinen .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T. (2011). Recognition of Actions. In: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision, vol 40. Springer, London. https://doi.org/10.1007/978-0-85729-748-8_9

Download citation

Publish with us

Policies and ethics

Search

Navigation