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Action Recognition Using Silhouette Sequences and Shape Descriptors

  • Katarzyna Gościewska
  • Dariusz FrejlichowskiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 525)

Abstract

The paper provides an approach for human action recognition based on shape analysis. The developed approach is intended for specific type of data, namely sequences of binary silhouettes representing a person performing an action, and consists of several processing steps including shape description as well as similarity or dissimilarity estimation. The approach can deal with sequences of different length without removing any frames. The paper also provides some experimental results showing the classification accuracy and overall recognition effectiveness of the proposed approach using several popular shape description algorithms, namely the Two-Dimensional Fourier Descriptor, Generic Fourier Descriptor, Point Distance Histogram and UNL-Fourier Descriptor.

References

  1. 1.
    Baysal, S., Kurt, M.C., Duygulu, P.: Recognizing human actions using key poses. In: 20th International Conference on Pattern Recognition, pp. 1727–1730, August 2010Google Scholar
  2. 2.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: The Tenth IEEE International Conference on Computer Vision, pp. 1395–1402 (2005)Google Scholar
  3. 3.
    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)CrossRefGoogle Scholar
  4. 4.
    Borges, P.V.K., Conci, N., Cavallaro, A.: Video-based human behavior understanding: a survey. IEEE Trans. Circ. Syst. Video Technol. 23(11), 1993–2008 (2013)CrossRefGoogle Scholar
  5. 5.
    Chaaraoui, A.A., Climent-Pérez, P., Flórez-Revuelta, F.: Silhouette-based human action recognition using sequences of key poses. Pattern Recogn. Lett. 34(15), 1799–1807 (2013)CrossRefGoogle Scholar
  6. 6.
    Chitode, J.: Digital Signal Processing. Technical Publications, Pune (2009)Google Scholar
  7. 7.
    Forczmański, P., Frejlichowski, D.: Robust stamps detection and classification by means of general shape analysis. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 360–367. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15910-7_41 CrossRefGoogle Scholar
  8. 8.
    Frejlichowski, D.: An experimental comparison of three polar shape descriptors in the general shape analysis problem. In: Swiatek, J., Borzemski, L., Grzech, A., Wilimowska, Z. (eds.) Information Systems Architecture and Technology – System Analysis in Decision Aided Problems, pp. 139–150. Oficyna Wydawnicza Politechniki Wrocławskiej (2010)Google Scholar
  9. 9.
    Frejlichowski, D.: Pre-processing, extraction and recognition of binary erythrocyte shapes for computer-assisted diagnosis based on mgg images. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L., Wojciechowski, K. (eds.) Computer Vision and Graphics, pp. 368–375. Springer, Berlin (2010)CrossRefGoogle Scholar
  10. 10.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007)CrossRefGoogle Scholar
  11. 11.
    Goudelis, G., Karpouzis, K., Kollias, S.: Exploring trace transform for robust human action recognition. Pattern Recogn. 46(12), 3238–3248 (2013)CrossRefGoogle Scholar
  12. 12.
    Junejo, I.N., Junejo, K.N., Aghbari, Z.A.: Silhouette-based human action recognition using sax-shapes. Vis. Comput. 30(3), 259–269 (2014)CrossRefGoogle Scholar
  13. 13.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 2, pp. 1137–1143. Morgan Kaufmann Publishers Inc., San Francisco (1995)Google Scholar
  14. 14.
    Kukharev, G.: Digital Image Processing and Analysis (in Polish). SUT Press, Szczecin (1998)Google Scholar
  15. 15.
    Liu, L., Shao, L., Zhen, X., Li, X.: Learning discriminative key poses for action recognition. IEEE Trans. Cybern. 43(6), 1860–1870 (2013)CrossRefGoogle Scholar
  16. 16.
    Rauber, T.W.: Two dimensional shape description. Technical report, Universidade Nova de Lisboa, Lisoba, Portugal (1994)Google Scholar
  17. 17.
    Vaswani, N., Roy-Chowdhury, A.K., Chellappa, R.: Shape activity: a continuous-state hmm for moving/deforming shapes with application to abnormal activity detection. IEEE Trans. Image Process. 14(10), 1603–1616 (2005)CrossRefGoogle Scholar
  18. 18.
    Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behavior understanding in video surveillance. Vis. Comput. 29(10), 983–1009 (2012)CrossRefGoogle Scholar
  19. 19.
    Zhang, D., Lu, G.: Shape-based image retrieval using generic fourier descriptor. Signal Process. Image Commun. 17(10), 825–848 (2002)CrossRefGoogle Scholar
  20. 20.
    Zhong, H., Shi, J., Visontai, M.: Detecting unusual activity in video. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-819-II-826, June 2004Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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