Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Motion of Oriented Magnitudes Patterns for Human Action Recognition

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

Included in the following conference series:

Abstract

In this paper, we present a novel descriptor for human action recognition, called Motion of Oriented Magnitudes Patterns (MOMP), which considers the relationships between the local gradient distributions of neighboring patches coming from successive frames in video. The proposed descriptor also characterizes the information changing across different orientations, is therefore very discriminative and robust. The major advantages of MOMP are its very fast computation time and simple implementation. Subsequently, our features are combined with an effective coding scheme VLAD (Vector of locally aggregated descriptors) in the feature representation step, and a SVM (Support Vector Machine) classifier in order to better represent and classify the actions. By experimenting on several common benchmarks, we obtain the state-of-the-art results on the KTH dataset as well as the performance comparable to the literature on the UCF Sport dataset.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. Pattern Anal. Mach. Intell. 35, 221–231 (2013)

    Article  Google Scholar 

  2. Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)

    Google Scholar 

  3. Le, Q.V., Zou, W.Y., Yeung, S.Y., Ng, A.Y.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: IEEE Conference on CVPR 2011, pp. 3361–3368. IEEE (2011)

    Google Scholar 

  4. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4489–4497. IEEE (2015)

    Google Scholar 

  5. Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64, 107–123 (2005)

    Article  Google Scholar 

  6. Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conference on CVPR 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  7. Wei, Q., Zhang, X., Kong, Y., Hu, W., Ling, H.: Group action recognition using space-time interest points. In: Bebis, G., et al. (eds.) ISVC 2009. LNCS, vol. 5876, pp. 757–766. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10520-3_72

    Chapter  Google Scholar 

  8. 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)

    Article  MathSciNet  Google Scholar 

  9. Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC 2009-British Machine Vision Conference, pp. 124:1–124:11. BMVA Press (2009)

    Google Scholar 

  10. Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551–3558 (2013)

    Google Scholar 

  11. Klaser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: BMVC 2008–19th British Machine Vision Conference, pp. 275:1–275:10. British Machine Vision Association (2008)

    Google Scholar 

  12. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  13. Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006). doi:10.1007/11744047_33

    Chapter  Google Scholar 

  14. Yeffet, L., Wolf, L.: Local trinary patterns for human action recognition. In: IEEE 12th International Conference on Computer Vision, pp. 492–497. IEEE (2009)

    Google Scholar 

  15. Kliper-Gross, O., Gurovich, Y., Hassner, T., Wolf, L.: Motion interchange patterns for action recognition in unconstrained videos. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 256–269. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_19

    Chapter  Google Scholar 

  16. Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: IEEE Conference on CVPR 2010, pp. 3304–3311. IEEE (2010)

    Google Scholar 

  17. Vu, N.-S., Caplier, A.: Face recognition with patterns of oriented edge magnitudes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 313–326. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15549-9_23

    Chapter  Google Scholar 

  18. Vu, N.S.: Exploring patterns of gradient orientations and magnitudes for face recognition. Inf. Forensics Secur. 8, 295–304 (2013)

    Article  Google Scholar 

  19. Jain, M., Jégou, H., Bouthemy, P.: Better exploiting motion for better action recognition. In: CVPR 2013, pp. 2555–2562 (2013)

    Google Scholar 

  20. Kantorov, V., Laptev, I.: Efficient feature extraction, encoding and classification for action recognition. In: Proceedings of the IEEE Conference on CVPR, pp. 2593–2600 (2014)

    Google Scholar 

  21. Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  22. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM TIST 2, 27 (2011)

    Google Scholar 

  23. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 32–36. IEEE (2004)

    Google Scholar 

  24. Rodriguez, M.D., Ahmed, J., Shah, M.: Action MACH a spatio-temporal maximum average correlation height filter for action recognition. In: 2008 IEEE Conference on CVPR, pp. 1–8. IEEE (2008)

    Google Scholar 

  25. Sadanand, S., Corso, J.J.: Action bank: a high-level representation of activity in video. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1234–1241. IEEE (2012)

    Google Scholar 

  26. Kovashka, A., Grauman, K.: Learning a hierarchy of discriminative space-time neighborhood features for har. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2046–2053. IEEE (2010)

    Google Scholar 

  27. Taylor, G.W., Fergus, R., LeCun, Y., Bregler, C.: Convolutional learning of spatio-temporal features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 140–153. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15567-3_11

    Chapter  Google Scholar 

  28. Liu, L., Shao, L., Li, X., Lu, K.: Learning spatio-temporal representations for action recognition: a genetic programming approach. IEEE Trans. Cybern. 46, 158–170 (2016)

    Article  Google Scholar 

  29. Kläser, A.: Learning human actions in video. Ph.D. thesis, Université de Grenoble (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ETIS - ENSEA/Universite de Cergy-Pontoise, CNRS UMR 8051, 95000, Cergy, France

    Hai-Hong Phan, Ngoc-Son Vu, Vu-Lam Nguyen & Mathias Quoy

Authors
  1. Hai-Hong Phan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ngoc-Son Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vu-Lam Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mathias Quoy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hai-Hong Phan .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Phan, HH., Vu, NS., Nguyen, VL., Quoy, M. (2016). Motion of Oriented Magnitudes Patterns for Human Action Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50832-0_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation