Advertisement

Human Action Recognition in Video

  • Dushyant Kumar SinghEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

In the world of automation every event needs some kind of auto response. Automatic responses can only be made when events are perceived automatically. With camera as a source of visual sensing, some intelligent system fitted with camera can make automatic visual perception possible for any event. Recognizing human activities for some automated response can be one challenge under this problem domain. In this paper, motion feature of a moving object is used for recognizing human action/activity. Histogram of Oriented Gradient (HOG) features with Support Vector Machine (SVM) classifier is used for classifying the human actions into 5 basic categories i.e. bending, boxing, handclapping, jogging and jumping. Pre-Processing involves Lucas-Kanade Algorithm to extract the human silhouette and Skeletonization operation to generate human skeletons. Skeletons are secondary features which are made input to SVM for activity classification. Experiments are conducted on KTH database and Weizmann database for accuracy calculation.

Keywords

Human action recognition Optical flow Lukas-Kanade Skeletonization HoG SVM 

References

  1. 1.
    Abu-Ain, W., Abdullah, S.N., Bataineh, B., Abu-Ain, T., Omar, K.: Skeletonization algorithm for binary images. Procedia Technol. 1(11), 704–709 (2013)CrossRefGoogle Scholar
  2. 2.
    Jati, A.N., Novamizanti, L., Prasetyo, M.B., Putra, A.R.: Evaluation of moving object detection methods based on general purpose single board computer. Indonesian J. Electr. Eng. Comput. Sci. 14(1), 123–129 (2015)Google Scholar
  3. 3.
    Goswami, P.P., Singh, D.K.: A hybrid approach for real-time object detection and tracking to cover background turbulence problem. Indian J. Sci. Technol. 9(45), 7 (2016)Google Scholar
  4. 4.
    Basavaraj, G.M., Kusagur, A.: Crowd anomaly detection using motion based spatio-temporal feature analysis. Indonesian J. Electr. Eng. Comput. Sci. 7(3), 737–747 (2017)Google Scholar
  5. 5.
    Singh, D.K., Kushwaha, D.S.: ILUT based skin colour modelling for human detection. Indian J. Sci. Technol. 9(32) (2016)Google Scholar
  6. 6.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I. IEEE (2001)Google Scholar
  7. 7.
    Singh, D.K., Kushwaha, D.S.: Automatic intruder combat system: a way to smart border surveillance. Defence Sci. J. 67(1), 50 (2017)CrossRefGoogle Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005, CVPR 2005, 25 June 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  9. 9.
    Kwak, N.J., Song, T.-S.: Human action classification and unusual action recognition algorithm for intelligent surveillance system. In: Kim, K.J., Chung, K.-Y. (eds.) IT Convergence and Security 2012. LNEE, vol. 215, pp. 797–804. Springer, Dordrecht (2013).  https://doi.org/10.1007/978-94-007-5860-5_95CrossRefGoogle Scholar
  10. 10.
    Kumar, N.S., Shobha, G.: Background modeling to detect foreground objects based on ANN and spatio-temporal analysis. Indonesian J. Electr. Eng. Comput. Sci. 2(1), 151–160 (2016)CrossRefGoogle Scholar
  11. 11.
    Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010)CrossRefGoogle Scholar
  12. 12.
    Patel, E., Shukla, D.: Comparison of optical flow algorithms for speed determination of moving objects. Int. J. Comput. Appl. 63(5), 32–37 (2013)Google Scholar
  13. 13.
    Aslani, S., Mahdavi-Nasab, H.: Optical flow based moving object detection and tracking for traffic surveillance. World Acad. Sci., Eng. Technol., Int. J. Electr., Comput., Energ., Electron. Commun. Eng. 7(9), 1252–1256 (2013)Google Scholar
  14. 14.
    Tschirren, J., Palágyi, K., Reinhardt, J.M., Hoffman, E.A., Sonka, M.: Segmentation, skeletonization, and branchpoint matching — a fully automated quantitative evaluation of human intrathoracic airway trees. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 12–19. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45787-9_2CrossRefzbMATHGoogle Scholar
  15. 15.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004, ICPR 2004, 23 August 2004, vol. 3, pp. 32–36. IEEE (2004)Google Scholar
  16. 16.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: 2005 Tenth IEEE International Conference on Computer Vision, ICCV 2005, 17 October 2005, vol. 2, pp. 1395–1402. IEEE (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSEMNNITAllahabadIndia

Personalised recommendations