Machine Vision and Applications

, Volume 23, Issue 3, pp 501–511

Abnormal crowd behavior detection using high-frequency and spatio-temporal features

Original Paper

Abstract

Abnormal crowd behavior detection is an important research issue in computer vision. The traditional methods first extract the local spatio-temporal cuboid from video. Then the cuboid is described by optical flow or gradient features, etc. Unfortunately, because of the complex environmental conditions, such as severe occlusion, over-crowding, etc., the existing algorithms cannot be efficiently applied. In this paper, we derive the high-frequency and spatio-temporal (HFST) features to detect the abnormal crowd behaviors in videos. They are obtained by applying the wavelet transform to the plane in the cuboid which is parallel to the time direction. The high-frequency information characterize the dynamic properties of the cuboid. The HFST features are applied to the both global and local abnormal crowd behavior detection. For the global abnormal crowd behavior detection, Latent Dirichlet allocation is used to model the normal scenes. For the local abnormal crowd behavior detection, Multiple Hidden Markov Models, with an competitive mechanism, is employed to model the normal scenes. The comprehensive experiment results show that the speed of detection has been greatly improved using our approach. Moreover, a good accuracy has been achieved considering the false positive and false negative detection rates.

Keywords

Local spatio-temporal cuboid Wavelet transform High-frequency information Latent Dirichlet allocation (LDA) Multiple Hidden Markov Models (HMMs) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Reisman, P., Mano, S.A.O., Shashua, A.: Crowd detection in video sequences. In: Proceedings of the IEEE Intelligent Vehicles Symposium, 2004, pp. 66–71, June 2004Google Scholar
  2. 2.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  3. 3.
    Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  4. 4.
    Helbing D., Molnár P., Farkas I.J., Bolay K.: Self-organizing pedestrian movement. Environ. Plan. B Plan. Des. 28, 361–383 (2001)CrossRefGoogle Scholar
  5. 5.
    Zhan B., Monekosso D.N., Remagnino P., Velastin S.A., Xu L.-Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19(5–6), 345–357 (2008)MATHCrossRefGoogle Scholar
  6. 6.
    Haering N., Venetianer P., Lipton A.: The evolution of video surveillance: an overview. Mach. Vis. Appl. 19(5), 279–290 (2008)MATHCrossRefGoogle Scholar
  7. 7.
    Tu, P., Sebastian, T., Doretto, G., Krahnstoever, N., Rittscher, J., Yu, T.: Unified crowd segmentation. ECCV, pp. 691–704 (2008)Google Scholar
  8. 8.
    Marques, J.S., Jorge, P.M., Abrantes, A.J., Lemos, J.M.: Tracking Groups of Pedestrians in Video Sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop 2003, vol. 9, pp. 101 (2003)Google Scholar
  9. 9.
    Chan, M.T., Hoogs, A., Schmiederer, J., Petersen, M.: Detecting rare events in video using semantic primitives with HMM. In: Proceedings of International Conference on Pattern Recognition, pp. 150–154 (2004)Google Scholar
  10. 10.
    Gryn, J., Wildes, R., Tsotsos, J.: Detecting motion patterns via direction maps with application to surveillance. In: Proceedings of the IEEE Workshop on Motion and Video Computing, pp. 202–209 (2005)Google Scholar
  11. 11.
    Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: Proceedings of British Machine Vision Conference, pp. 583–592 (1995)Google Scholar
  12. 12.
    Chan, M.T., Hoogs, A., Schmiederer, J., Petersen, M.: Detecting rare events in video using semantic primitives with HMM. In: Proceedings of International Conference on Pattern Recognition, pp. 150–154 (2004)Google Scholar
  13. 13.
    Dee, H., Hogg, D.: Detecting inexplicable behaviour. In: Proceedings of British Macine Vision Conference, pp. 477–486 (2004)Google Scholar
  14. 14.
    Chan, A.B., Vasconcelos, N.: Mixtures of dynamic textures. In: ICCV’05: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 641–647. Washington, DC, USA, IEEE Computer Society (2005)Google Scholar
  15. 15.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Modeling crowd scenes for event detection. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), vol. 01, pp. 175–178. IEEE Computer Society Washington, DC (2006)Google Scholar
  16. 16.
    Ali, S., Shah, M.: A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR’07, pp. 1–6, June 2007Google Scholar
  17. 17.
    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: ECCV’08: Proceedings of the 10th European Conference on Computer Vision, pp. 1–14. Springer, Berlin (2008)Google Scholar
  18. 18.
    Black, M.: Explaining optical flow events with parameterized spatio-temporal models. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 326–332 (1999)Google Scholar
  19. 19.
    Helbing D., Molnar P.: Social force model for pedestrian dynamics. Phys. Rev. E 51, 4282 (1995)CrossRefGoogle Scholar
  20. 20.
    Blei D., Ng A., Jordan M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  21. 21.
    Burrus C.S., Gopinath R.A., Guo H.: Introduction to wavelets and wavelet transforms, pp. 7458. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
  22. 22.
    Cohen A., Daubechies I., Feauveau J.-C.: Biorthogonal bases of compactly supported wavelets. Commun. Pure Appl. Math. 45(5), 485–560 (1992)MathSciNetMATHCrossRefGoogle Scholar
  23. 23.
    Jolliffe I.T.: Principal Component Analysis. Springer, New York (1986)Google Scholar
  24. 24.
    Fei-Fei, L., Fergus, R., Torralba, A.: Recognizing and learning object categories. Tutorial at ICCV (2005)Google Scholar
  25. 25.
    Dempster A.P., Laird N.M., Rubin D.R.: Maximum likelihood from incomplete date via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)MathSciNetMATHGoogle Scholar
  26. 26.
    Bishop, C.M.: Pattern Recognition and Machine Learning. pp. 76–78. Springer, New York (2006)Google Scholar
  27. 27.
    Andrade, E., Fisher, R.: Hidden Markov models for optical flow analysis in crowds. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR06), vol. 01, pp. 460C463. IEEE Computer Society Washington (2006)Google Scholar
  28. 28.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. In: Proceedings of IEEE, vol. 77, no. 2, pp. 257–285 (1989)Google Scholar
  29. 29.
    Gui L., Thiran J.P., Paragios N.: Cooperative object segmentation and behavior inference in image sequences. Int. J. Comput. Vis. 84(2), 146–162 (2009)CrossRefGoogle Scholar
  30. 30.
    Unusual crowd activity dataset of University of Minnesota. Available from http://mha.cs.umn.edu/movies/crowdactivity-all.avi

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Bo Wang
    • 1
    • 2
  • Mao Ye
    • 1
    • 2
  • Xue Li
    • 3
  • Fengjuan Zhao
    • 1
    • 2
  • Jian Ding
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  3. 3.School of Information Technology and Electronic EngineeringThe University of QueenslandBrisbaneAustralia

Personalised recommendations