Skip to main content
SpringerLink
Log in

Novel Human Action Recognition in RGB-D Videos Based on Powerful View Invariant Features Technique

  • Chapter
  • First Online:
Modern Approaches for Intelligent Information and Database Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 769))

Abstract

Human action recognition is one of the important topic in nowadays research. It is obstructed by several factors, among them we can enumerate: the variation of shapes and postures of a human been, the time and memory space need to capture, store, label and process those images. In addition, recognize a human action from different view point is challenging due to the big amount of variation in each view, one possible solution of mentioned problem is to study different preferential View-invariant features sturdy enough to view variation. Our focus on this paper will be to solve mentioned problem by learning view shared and view specific features applying innovative deep models known as a novel sample-affinity matrix (SAM), able to give a good measurement of the similarities among video samples in different camera views. This will also lead to precisely adjust transmission between views and study more informative shared features involve in cross-view actions classification. In addition, we are proposing in this paper a novel view invariant features algorithm, which will give us a better understanding of the internal processing of our project. We have demonstrated through a series of experiment apply on NUMA and IXMAS (multiple camera view video dataset) that our method out performs state-of-the-art-methods.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Fu, Y., Kong, Y.: Bilinear heterogeneous information machine for RGB-D action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1054–1062 (2015)

    Google Scholar 

  2. Fu, Y., Kong, Y.: Max-margin action prediction machine. IEEE Trans. Pattern Anal. Mach. Intell. 38, 1844–1858 (2016)

    Article  Google Scholar 

  3. Barshan, B., Tunçel, O., Altun, K.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognit. 43, 3605–3620 (2010)

    Article  MATH  Google Scholar 

  4. Nanopoulos, A., Schmidt-Thieme, L., Grabocka, J.: Classification of sparse time series via supervised matrix factorization. In: Proceeding of the AAAI, pp. 928–934 (2012)

    Google Scholar 

  5. Dexter, E., Laptev, I., Pérez, P., Junejo, I. N.: Cross-view action recognition from temporal self-similarities. In: Proceeding of the ECCV, pp. 293–306 (2008)

    Google Scholar 

  6. Gao, Y., Shi, Y., Cao, L., Yang, W.: MRM-lasso: a sparse multiview feature selection method via low-rank analysis. IEEE Trans. Neural Netw. 26, 2801–2815 (2015)

    Article  MathSciNet  Google Scholar 

  7. Ricci, E., Subramanian, S., Liu, G., Sebe, N., Yan, Y.: Multitask linear discriminant analysis for view invariant action recognition. 23, 5599–5611 (2014)

    Google Scholar 

  8. Jiang, Z., Zheng, J., Phillips, J., Chellappa, R.: Cross-view action recognition via a transferable dictionary pair. In: Proceeding of the British Machine Vision Conference, pp. 125.1–125.11 (2012)

    Google Scholar 

  9. Kong, Y., Ding, Z., Li, J., Fu, Y.: Deeply learned view-invariant features for cross-view action recognition. IEEE Trans. Image Process. (2017)

    Google Scholar 

  10. Xu, Z., Weinberger, K., Sha, F., Chen, M.: Marginalized denoising autoencoders for domain adaptation. In: Proceeding of the ICML, pp. 1627–1634 (2012)

    Google Scholar 

  11. Guo, Y., Zhou, J., Ding, G.: Collective matrix factorization hashing for multimodal data. In: Proceeding of the CVPR, pp. 2075–2082 (2014)

    Google Scholar 

  12. Daumé, H., Kumar, A.: A co-training approach for multi-view spectral clustering. In: Proceeding of the ICML, pp. 393–400 (2011)

    Google Scholar 

  13. Zhang, K., Gu, P., Xue, X., Zhang, W.: Multi-view embedding learning for incompletely labeled data. In: Proceeding of the IJCAI, pp. 1910–1916 (2013)

    Google Scholar 

  14. He, R., Wang, W., Wang, L., Tan, T., Wang, K.: Learning coupled feature spaces for cross-modal matching. In Proceeding of the ICCV, pp. 2088–2095 (2013)

    Google Scholar 

  15. Tao, D., Xu, C., Xu, C.: Multi-view learning with incomplete views. IEEE Trans. Image Process. 24, 5812–5825 (2015)

    Article  MathSciNet  Google Scholar 

  16. Kumar, A., Daume, H., Jacobs, D.W., Sharma, A.: Generalized multiview analysis: a discriminative latent space. In: Proceeding of the CVPR, pp. 2160–2167 (2012)

    Google Scholar 

  17. Fu, Y., Ding, Z.: Low-rank common subspace for multi-view learning. In Proceeding of the IEEE International Conference on Data Mining (ICDM), pp. 110–119 (2014)

    Google Scholar 

  18. Özuysal, M., Fua, P., Weinland, D.: Making action recognition robust to occlusions and viewpoint changes. In: Proceeding of the ECCV, pp. 635–648 (2010)

    Google Scholar 

  19. Dexter, E., Laptev, I., Pérez, P., Junejo, I.N.: Cross-view action recognition from temporal self-similarities. In: Proceeding of the ECCV, pp. 293–306 (2008)

    Google Scholar 

  20. Dexter, E., Laptev, I., Perez, P., Junejo, I.N.: View-independent action recognition from temporal self-similarities. IEEE Trans. Pattern Anal. Mach. Intell. 33, 172–185 (2011)

    Article  Google Scholar 

  21. Mian, A., Rahmani, H.: Learning a non-linear knowledge transfer model for cross-view action recognition. In: Proceeding of the CVPR, pp. 2458–2466 (2015)

    Google Scholar 

  22. Shah, M., Kuipers, B., Savarese, S., Liu, J.: Cross-view action recognition via view knowledge transfer. In: Proceeding of the CVPR, pp. 3209–3216, June 2011

    Google Scholar 

  23. Jiang, Z., Zheng, J.: Learning view-invariant sparse representations for cross-view action recognition. In: Proceeding of the ICCV, pp. 3176–3183 (2013)

    Google Scholar 

  24. Shan, S., Zhang, H., Lao, S., Chen, X., Kan, M.: Multi-view discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38, 188–194 (2016)

    Article  Google Scholar 

  25. Camps, O.I., Sznaier, M., Li, B.: Cross-view activity recognition using hankelets. In: Proceeding of the CVPR, pp. 1362–1369 (2012)

    Google Scholar 

  26. Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C., Zhang, Z.: Cross- view action recognition via a continuous virtual path. In: Proceeding of the CVPR, pp. 2690–2697 (2013)

    Google Scholar 

  27. Salakhutdinov, R.R., Hinton, G.E.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  28. Zhang, T., Luo, W., Yang, J., Yuan, X., Zhang, J., Li, J.: Sparseness analysis in the pretraining of deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. (to be published). https://doi.org/10.1109/tnnls.2016.2541681

  29. Weinberger, K., Sha, F., Bengio, Y., Chen, M.: Marginalized denoising auto-encoders for nonlinear representations. In: Proceeding of the ICML, pp. 1476–1484 (2014)

    Google Scholar 

  30. Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A., Vincent, P.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  32. Tabrizi, M.K., Endres, I., Forsyth, D.A., Farhadi, A.: A latent model of discriminative aspect. In: Proceeding of the ICCV, pp. 948–955 (2009)

    Google Scholar 

  33. Martinez, J., Little, J.J., Woodham, R.J., Gupta, A.: 3D pose from motion for cross-view action recognition via non-linear circulant temporal encoding. In: Proceeding of the CVPR, pp. 2601–2608 (2014)

    Google Scholar 

  34. Nie, X., Xia, Y., Wu, Y., Zhu, S.C., Wang, J.: Cross-view action modeling, learning and recognition. In: Proceeding of the CVPR, pp. 2649–2656 (2014)

    Google Scholar 

  35. Rabaud, V., Cottrell, G., Belongie, S., Dollar, P.: Behavior recognition via sparse spatio-temporal features. In: Proceeding of the VS-PETS, pp. 65–72, Oct 2005

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work and the contribution were supported by The Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic.

Author information

Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Kralove, Czech Republic

    Sebastien Mambou, Ondrej Krejcar, Kamil Kuca & Ali Selamat

  2. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Baharu, Johor, Malaysia

    Ali Selamat

Authors
  1. Sebastien Mambou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ondrej Krejcar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamil Kuca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Selamat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Andrzej Sieminski

  2. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

  3. Department of Information Systems and Computing, Complutense University of Madrid, Madrid, Spain

    Manuel Nunez

  4. Faculty of Information Technology, Vietnam National University, Hanoi, Vietnam

    Quang Thuy Ha

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mambou, S., Krejcar, O., Kuca, K., Selamat, A. (2018). Novel Human Action Recognition in RGB-D Videos Based on Powerful View Invariant Features Technique. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76081-0_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76080-3

  • Online ISBN: 978-3-319-76081-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation