Skip to main content

Efficient Pose-Based Action Recognition

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9007)

Abstract

Action recognition from 3d pose data has gained increasing attention since the data is readily available for depth or RGB-D videos. The most successful approaches so far perform an expensive feature selection or mining approach for training. In this work, we introduce an algorithm that is very efficient for training and testing. The main idea is that rich structured data like 3d pose does not require sophisticated feature modeling or learning. Instead, we reduce pose data over time to histograms of relative location, velocity, and their correlations and use partial least squares to learn a compact and discriminative representation from it. Despite of its efficiency, our approach achieves state-of-the-art accuracy on four different benchmarks. We further investigate differences of 2d and 3d pose data for action recognition.

Keywords

  • Partial Little Square
  • Video Clip
  • Action Recognition
  • Dynamic Time Warping
  • Human Action Recognition

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-16814-2_28
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-16814-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Notes

  1. 1.

    http://research.microsoft.com/en-us/um/people/zliu/ActionRecoRsrc/.

References

  1. Campbell, L., Bobick, A.: Recognition of human body motion using phase space constraints. In: ICCV (1995)

    Google Scholar 

  2. Bissacco, A., Chiuso, A., Ma, Y., Soatto, S.: Recognition of human gaits. In: CVPR (2001)

    Google Scholar 

  3. Wu, S., Oreifej, O., Shah, M.: Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories. In: ICCV (2011)

    Google Scholar 

  4. Efros, A., Berg., A., Mori, G., Malik, J.: Recognizing action at a distance. In: CVPR (2003)

    Google Scholar 

  5. Thurau, C., Hlavac, V.: Pose primitive based human action recognition in videos or still images. In: CVPR (2008)

    Google Scholar 

  6. Ikizler-Cinbis, N., Cinbis, R., Sclaroff, S.: Learning actions from the web. In: ICCV (2009)

    Google Scholar 

  7. Eweiwi, A., Cheema, M.S., Bauckhage, C.: Discriminative joint non-negative matrix factorization for human action classification. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 61–70. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  8. Laptev, I.: On space-time interest points. IJCV 64, 107–123 (2005)

    CrossRef  Google Scholar 

  9. Willems, G., Tuytelaars, T., Van Gool, L.: An efficient dense and scale-invariant spatio-temporal interest point detector. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 650–663. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  10. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)

    Google Scholar 

  11. Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103, 60–79 (2013)

    CrossRef  MathSciNet  Google Scholar 

  12. Xia, L., Aggarwal, J.: Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: CVPR (2013)

    Google Scholar 

  13. Ye, M., Zhang, Q., Wang, L., Zhu, J., Yang, R., Gall, J.: A survey on human motion analysis from depth data. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds.) Time-of-Flight and Depth Imaging. LNCS, vol. 8200, pp. 149–187. Springer, Heidelberg (2013)

    Google Scholar 

  14. Yang, Y., Ramanan, D.: Articulated human detection with flexible mixtures of parts. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2878–2890 (2013)

    CrossRef  Google Scholar 

  15. Shotton, J., Girshick, R.B., Fitzgibbon, A.W., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A.: Efficient human pose estimation from single depth images. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2821–2840 (2013)

    CrossRef  Google Scholar 

  16. Yao, A., Gall, J., van Gool, L.: Coupled action recognition and pose estimation from multiple views. Int. J. Comput. Vis. 100, 16–37 (2012)

    CrossRef  MATH  Google Scholar 

  17. Tran, K.N., Kakadiaris, I.A., Shah, S.K.: Modeling motion of body parts for action recognition. In: BMVC (2011)

    Google Scholar 

  18. Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.: Towards understanding action recognition. In: ICCV (2013)

    Google Scholar 

  19. Wang, C., Wang, Y., Yuille, A.: An approach to pose-based action recognition. In: CVPR (2013)

    Google Scholar 

  20. Wang, J., Liu, Z., Liu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: CVPR (2012)

    Google Scholar 

  21. Zanfir, M., Leordeanu, M., Sminchisescu, C.: The moving pose: an efficient 3D kinematics descriptor for low-latency action recognition and detection. In: ICCV (2013)

    Google Scholar 

  22. Wanqing, L., Zhengyou, Z., Zicheng, L.: Action recognition based on a bag of 3D points. In: CVPRW (2010)

    Google Scholar 

  23. Oreifej, O., Liu, Z.: Hon4d: Histogram of oriented 4D normals for activity recognition from depth sequences. In: CVPR (2013)

    Google Scholar 

  24. Barker, M., Rayens, W.: Partial least squares for discrimination. J. Chemometr. 17, 166–173 (2003)

    CrossRef  Google Scholar 

  25. Hajd, M.A., Gonzlez, J., Davis, L.: On partial least squares in head pose estimation: how to simultaneously deal with misalignment. In: CVPR (2012)

    Google Scholar 

  26. Harada, T., Ushiku, Y., Yamashita, Y., Kuniyoshi, Y.: Discriminative spatial pyramid. In: CVPR (2011)

    Google Scholar 

  27. Schwartz, W.R., Kembhavi, A., Harwood, D., Davis, L.S.: Human detection using partial least squares analysis. In: ICCV (2009)

    Google Scholar 

  28. Sharma, A., Jacobs, D.: Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In: CVPR (2011)

    Google Scholar 

  29. Rosipal, R., Be, P.P., Trejo, L.J., Cristianini, N., Shawe-Taylor, J., Williamson, B.: Kernel partial least squares regression in reproducing Kernel Hilbert space. JMLR 2, 97–123 (2001)

    Google Scholar 

  30. Tenorth, M., Bandouch, J., Beetz, M.: The TUM Kitchen data set of everyday manipulation activities for motion tracking and action recognition. In: ICCV Workshops (2009)

    Google Scholar 

  31. Li, M., Yuan, B.: 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn. Lett. 26, 527–532 (2005)

    CrossRef  Google Scholar 

  32. Bauckhage, C., Käster, T.: Benefits of separable, multilinear discriminant classification. In: ICPR (2006)

    Google Scholar 

  33. Wang, J., Wu, Y.: Learning maximum margin temporal warping for action recognition. In: ICCV (2013)

    Google Scholar 

  34. Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3D action recognition with random occupancy patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 872–885. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  35. Vemulapalli, R., Arrate, F., Chellappa, R.: Human action recognition by representing 3D skeletons as points in a lie group. In: CVPR (2014)

    Google Scholar 

Download references

Acknowledgment

This work was carried out in the project automatic activity recognition in large image databases which is funded by the German Research Foundation (DFG). The authors would also like to acknowledge the financial support provided by the DFG Emmy Noether program (GA 1927/1-1).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abdalrahman Eweiwi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Eweiwi, A., Cheema, M.S., Bauckhage, C., Gall, J. (2015). Efficient Pose-Based Action Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16814-2_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

  • eBook Packages: Computer ScienceComputer Science (R0)