Advertisement

Real-Time Abnormal Events Detection Combining Motion Templates and Object Localization

  • Thi-Lan LeEmail author
  • Thanh-Hai Tran
Conference paper
  • 426 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 341)

Abstract

Recently, abnormal event detection has attracted great research attention because of its wide range of applications. In this paper, we propose an hybrid method combining both tracking output and motion templates. This method consists of two steps: object detection, localization and tracking and abnormal event detection. Our contributions in this paper are three-folds. Firstly, we propose a method that apply only HOG-SVM detector on extended regions detected by background subtraction. This method takes advantages of the background subtraction method (fast computation) and the HOG-SVM detector (reliable detection). Secondly, we do multiple objects tracking based on HOG descriptor. The HOG descriptor, computed in the detection phase, will be used in the phase of observation and track association. This descriptor is more robust than usual grayscale (color) histogram based descriptor. Finally, we propose a hybrid method for abnormal event detection this allows to remove several false detection cases.

Keywords

Video analysis Event recognition Object detection and tracking 

Notes

Acknowledgments

The research leading to this paper was supported by the National Project B2013.01.41 “Study and develop an abnormal event recognition system based on computer vision techniques”. We would like to thank the project and people involved in this project.

References

  1. 1.
    Basharat, A., Gritai, A., Shan, M.: Learning object motion patterns for anomaly detection and improved object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–8 June 2008Google Scholar
  2. 2.
    Benezeth, Y., Jodoin, P.M., Saligrama, V., Rosen-berger, C.: Abnormal events detection based on spatio-temporal co-occurences. 2013 IEEE Conference on ComputerVision and Pattern Recognition, pp. 2458–2465 (2009)Google Scholar
  3. 3.
    Rougier, C., St-Arnaud, A., Rousseau, J., Meunier, J.: Video surveillance for fall detection. In: Lin, P.W. (ed.) Vid. Surveill. (2011)Google Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR 05), vol. 1, pp. 886–893 June 2005Google Scholar
  5. 5.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3), 172–185 (2005)CrossRefGoogle Scholar
  6. 6.
    Ribeiro, M.I.: Kalman and extended Kalman filters: concept, derivation and properties. Technical report (2004)Google Scholar
  7. 7.
    Noury, N., Rumeau, P., Bourke, A., Laighin, G., Lundy, J.: A proposal for the classification and evaluation of fall detectors. (IRBM) 29(6), 340–349 (2008)Google Scholar
  8. 8.
    Vishwakarma, V., Mandal, C., Sural, S.: Automatic detection of human fall in video. In: Ghosh, A., De, R., Pal, S. (eds.) Pattern Recognition and Machine Intelligence. Lecture Notes in Computer Science, vol. 4815, pp. 616–623. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.International Research Institute MICAHUST - CNRS/UMI-2954 - Grenoble INP and Hanoi University of Science and TechnologyHanoiVietnam

Personalised recommendations