HoP: Histogram of Patterns for Human Action Representation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)


This paper presents a novel method for representing actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. This paper proposes to learn a codebook of frequent sequential patterns by means of an apriori-like algorithm, and to represent an action with a Bag-of-Frequent-Sequential-Patterns approach. Preliminary experiments of the proposed method have been conducted for action classification on skeletal data. The method achieves state-of-the-art accuracy value in cross-subject validation.


Action classification Apriori algorithm Frequent pattern 



We are grateful to Mr. Giovanni Caruana for making available his implementation of the classic apriori algorithm, which he implemented in his Master thesis work at University of Palermo.


  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD record, vol. 22. no. 2. ACM (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering. IEEE (1995)Google Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1. IEEE (2005)Google Scholar
  4. 4.
    Demirdjian, D., Wang, S.: Recognition of temporal events using multiscale bags of features. In: IEEE Workshop on Computational Intelligence for Visual Intelligence (CIVI). IEEE (2009)Google Scholar
  5. 5.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: IEEE Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2. IEEE (2005)Google Scholar
  6. 6.
    Gavrilov, Z., Sclaroff, S., Neidle, C., Dickinson, S.: Detecting reduplication in videos of american sign language. In: Proceedings of Eighth International Conference on Language Resources and Evaluation (LREC), Instanbul, Turkey, May 2012Google Scholar
  7. 7.
    Hussein, M.E., et al.: Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations. In: IJCAI, vol. 13 (2013)Google Scholar
  8. 8.
    Karaman, S., et al.: L1-regularized logistic regression stacking and transductive CRF smoothing for action recognition in video. In: ICCV workshop on action recognition with a large number of classes, vol. 13 (2013)Google Scholar
  9. 9.
    Laptev, I., et al.: Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2008)Google Scholar
  10. 10.
    Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana 31(2), 173–198 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Presti, L.L., La Cascia, M.: 3D skeleton-based human action classification: a survey. Pattern Recogn. 53, 130–147 (2016)CrossRefGoogle Scholar
  12. 12.
    Murthy, O.V., Goecke, R.: Ordered trajectories for large scale human action recognition. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2013)Google Scholar
  13. 13.
    Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 79(3), 299–318 (2008)CrossRefGoogle Scholar
  14. 14.
    Peng, X., et al.: Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput. Vis. Image Underst. 150, 109–125 (2016)CrossRefGoogle Scholar
  15. 15.
    Peng, X., et al.: Exploring motion boundary based sampling and spatial-temporal context descriptors for action recognition. In: British Machine Vision Conference (BMVC) (2013)Google Scholar
  16. 16.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR), vol. 3. IEEE (2004)Google Scholar
  17. 17.
    Wang, H., et al.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Fothergill, S., et al.: Instructing people for training gestural interactive systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2012)Google Scholar
  19. 19.
    Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)CrossRefGoogle Scholar
  20. 20.
    Wang, C., Wang, Y., Yuille, A.L.: An approach to pose-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)Google Scholar
  21. 21.
    Wang, J., et al.: Mining actionlet ensemble for action recognition with depth cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2012)Google Scholar
  22. 22.
    Zhao, X., et al.: Online human gesture recognition from motion data streams. In: Proceedings of the 21st ACM international conference on Multimedia. ACM (2013)Google Scholar
  23. 23.
    Zhu, Y., Zhao, X., Fu, Y., Liu, Y.: Sparse coding on local spatial-temporal volumes for human action recognition. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6493, pp. 660–671. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19309-5_51 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universita’ degli Studi di PalermoPalermoItaly

Personalised recommendations