Skip to main content
SpringerLink
Log in

Action identification using a descriptor with autonomous fragments in a multilevel prediction scheme

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Recent technological advances have provided powerful devices with high processing and storage capabilities. Video cameras can be found in several different areas, such as banks, schools, stores, public streets and industry for a variety of tasks. Camera technology has increasingly improved, achieving higher resolution and acquisition frame rates. Nevertheless, most video analysis tasks are performed by human operators, whose performance may be affected by fatigue and stress. To address this problem, this work proposes and evaluates a method for action identification in videos through a new descriptor composed of autonomous fragments applied to a multilevel prediction scheme. The method is very fast and achieves over 90 % of accuracy in known public data sets. The developed system allows for the current video cameras the possibility of real-time action analysis, demonstrating to be a useful and powerful tool for surveillance purpose.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Alcantara, M.F., Moreira, T.P., Pedrini, H.: Motion silhouette-based real time action recognition. In: 18th Iberoamerican Congress on Pattern Recognition, vol. 8259 (LNCS), pp. 471–478 (2013)

  2. Alcantara, M.F., Moreira, T.P., Pedrini, H.: Real-time action recognition based on cumulative motion shapes. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2917–2921, Florence, Italy, 4–9, May (2014)

  3. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: International Conference on Computer Vision, pp. 1395–1402 (2005)

  4. Bregonzio, M., Xiang, T., Gong, S.: Fusing appearance and distribution information of interest points for action recognition. Pattern Recognit. 45(3), 1220–1234 (2012)

    Article  Google Scholar 

  5. Cai, J.X., Tang, X., Feng, G.C.: Learning pose dictionary for human action recognition. In: International Conference on Pattern Recognition, pp. 381–386 (2014)

  6. Chaaraoui, A., Climent-Pérez, P., Flórez-Revuelta, F.: Silhouette-based human action recognition using sequences of key poses. Pattern Recognit. Lett. 34(15), 1799–1807 (2013)

    Article  Google Scholar 

  7. Chaaraoui, A., Flórez-Revuelta, F.: Human action recognition optimization based on evolutionary feature subset selection. In: Genetic and Evolutionary Computation Conference, pp. 1229–1236 (2013)

  8. Cheema, S., Eweiwi, A., Thurau, C., Bauckhage, C.: Action recognition by learning discriminative key poses. In: International Conference on Computer Vision, pp. 1302–1309 (2011)

  9. Guo, K., Ishwar, P., Konrad, J.: Action recognition from video using feature covariance matrices. IEEE Trans. Image Process. 22(6), 2479–2494 (2013)

    Article  MathSciNet  Google Scholar 

  10. Hsieh, C.H., Huang, P., Tang, M.D.: The recognition of human action using silhouette histogram. In: Reynolds, M. (ed.) Australasian Computer Science Conference, vol. 113, pp. 11–16 (2011)

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

    Article  Google Scholar 

  12. Junejo, I.N., Aghbari, Z.A.: Using SAX representation for human action recognition. J. Vis. Commun. Image Represent. 23(6), 853–861 (2012)

    Article  Google Scholar 

  13. Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: European Workshop on Advanced Video Based Surveillance Systems, vol. 5308 (2001)

  14. Karthikeyan, S., Gaur, U., Manjunath, B.S., Grafton, S.: Probabilistic subspace-based learning of shape dynamics modes for multi-view action recognition. In: International Conference on Computer Vision, pp. 1282–1286 (2011)

  15. Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)

    Article  Google Scholar 

  16. Mahbub, U., Imtiaz, H., Ahad, M.A.R.: Action recognition based on statistical analysis from clustered flow vectors. Signal Image Video Process. 8(2), 243–253 (2014)

    Article  Google Scholar 

  17. Moghaddam, Z., Piccardi, M.: Histogram-based training initialisation of hidden markov models for human action recognition. In: International Conference on Advanced Video and Signal Based Surveillance, pp. 256–261 (2010)

  18. Moghaddam, Z., Piccardi, M.: Training initialization of hidden Markov models in human action recognition. Autom. Sci. Eng. 36(99), 1–15 (2013)

    Google Scholar 

  19. Onofri, L., Soda, P.: Combining video subsequences for human action recognition. In: International Conference on Pattern Recognition, pp. 597–600 (2012)

  20. Raja, K., Laptev, I., Perez, P., Oisel, L.: Joint pose estimation and action recognition in image graphs. In: International Conference on Image Processing, pp. 25–28 (2011)

  21. Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: International Conference on Computer Vision, pp. 1593–1600 (2009)

  22. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: 17th International Conference on Pattern Recognition, pp. 32–36 (2004)

  23. Singh, S., Velastin, S.A., Ragheb, H.: MuHAVi: A multicamera human action video dataset for the evaluation of action recognition methods. In: Advanced Video and Signal Based Surveillance, pp. 48–55 (2010)

  24. Sun, X., Chen, M., Hauptmann, A.: Action recognition via local descriptors and holistic features. In: Computer Vision and Pattern Recognition, pp. 58–65 (2009)

  25. Ta, A.P., Wolf, C., Lavoue, G., Baskurt, A., Jolion, J.M.: Pairwise features for human action recognition. In: International Conference on Pattern Recognition, pp. 3224–3227 (2010)

  26. Tsai, D.M., Chiu, W.Y., Lee, M.H.: Optical flow-motion history image (OF-MHI) for action recognition. Signal Image Video Process. 9(8), 1897–1906 (2015)

    Article  Google Scholar 

  27. Wang, S., Huang, K., Tan, T.: A compact optical flowbased motion representation for real-time action recognition in surveillance scenes. In: International Conference on Image Processing, pp. 1121–1124 (2009)

  28. Wu, C., Khalili, A.H., Aghajan, H.: Multiview activity recognition in smart homes with spatio-temporal features. In: International Conference on Distributed Smart Cameras, pp. 142–149 (2010)

  29. Zhang, Z., Tao, D.: Slow feature analysis for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 436–450 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to FAPESP (Grants #2011/22749-8 and #2012/20738-1) and CNPq (Grant #307113/2012-4) for their financial support.

Author information

Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, SP, 13083-852, Brazil

    Marlon Fernandes de Alcantara, Thierry Pinheiro Moreira & Helio Pedrini

  2. Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames, KT1 2EE, UK

    Francisco Flórez-Revuelta

Authors
  1. Marlon Fernandes de Alcantara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thierry Pinheiro Moreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Helio Pedrini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francisco Flórez-Revuelta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Helio Pedrini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Alcantara, M.F., Moreira, T.P., Pedrini, H. et al. Action identification using a descriptor with autonomous fragments in a multilevel prediction scheme. SIViP 11, 325–332 (2017). https://doi.org/10.1007/s11760-016-0940-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0940-3

Keywords

Search

Navigation