Advertisement

Weighted Interaction Force Estimation for Abnormality Detection in Crowd Scenes

  • Xiaobin Zhu
  • Jing Liu
  • Jinqiao Wang
  • Wei Fu
  • Hanqing Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

In this paper, we propose a weighted interaction force estimation in the social force model(SFM)-based framework, in which the properties of surrounding individuals in terms of motion consistence, distance apart, and angle-of-view along moving directions are fully utilized in order to more precisely discriminate normal or abnormal behaviors of crowd. To avoid the challenges in object tracking in crowded videos, we first perform particle advection to capture the continuity of crowd flow and use these moving particles as individuals for the interaction force estimation. For a more reasonable interaction force estimation, we jointly consider the properties of surrounding individuals, assuming that the individuals with consistent motion (as a particle group) and the ones out of the angle-of-view have no influence on each other, besides the farther apart ones have weaker influence. In particular, particle groups are clustered by spectral clustering algorithm, in which a novel and high discriminative gait feature in frequency domain, combined with spatial and motion feature, is used. The estimated interaction forces are mapped to image span to form force flow, from which bag-of-word features are extracted. Sparse Topical Coding (STC) model is used to find abnormal events. Experiments conducted on three datasets demonstrate the promising performance of our work against other related ones.

Keywords

Interaction Force Visual Word Latent Dirichlet Allocation Abnormal Event Gait Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Physical Review E 51, 42–82 (1995)Google Scholar
  2. 2.
    Sand, P., Teller, S.: Particle video: Long-range motion estimation using point trajectories. IJCV (2008)Google Scholar
  3. 3.
    Zhu, J., Xing, E.: Sparse topical coding. In: UAI (2011)Google Scholar
  4. 4.
    Chan, M., Hoogs, A., Schmiederer, J., Petersen, M.: Detecting rare events in video using semantic primitives with hmm. In: ICPR (2004)Google Scholar
  5. 5.
    Dee, H., Hogg, D.: Detecting inexplicable behavior. In: BMVC (2004)Google Scholar
  6. 6.
    Tu, P., Sebastian, T., Doretto, G., Krahnstoever, N., Rittscher, J., Yu, T.: Unified Crowd Segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 691–704. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: CVPR (2009)Google Scholar
  8. 8.
    Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: CVPR (2009)Google Scholar
  9. 9.
    Yang, C., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: CVPR (2011)Google Scholar
  10. 10.
    Wu, S., Moore, B., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: CVPR (2010)Google Scholar
  11. 11.
    Ali, S., Sha, M.: A lgrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: CVPR (2007)Google Scholar
  12. 12.
    Mehran, R., Moore, B.E., Shah, M.: A Streakline Representation of Flow in Crowded Scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 439–452. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Raghavendra, R., Alessio, D., Macro, C., Vittorio, M.: Optimizing interaction force for global anomaly detection in crowded scenes. In: ICCV Workshops (2011)Google Scholar
  14. 14.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. JMLR 34, 993–1022 (1981)Google Scholar
  15. 15.
    Lekien, F., Marsden, J.: Tricubic interpolation in three dimensions. Journal of Numerical Methods and Engineering 63, 455–471 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: Experiments, simulations and design solutions. Transportation Science 39, 1–24 (2005)CrossRefGoogle Scholar
  17. 17.
    Fu, W., Wang, J., Li, Z., Lu, H., Ma, S.: Learning semantic motion patterns for dynamic scenes by improved sparse topical coding. In: ICME (2012)Google Scholar
  18. 18.
    Sochman, J., Hogg, D.: Who knows who – inverting the social force model for finding groups. In: ICCV Workshops (2011)Google Scholar
  19. 19.
    Brostow, G., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. In: CVPR (2006)Google Scholar
  20. 20.
    Rabaud, V., Belongie, S.: Counting crowded moving objects. In: CVPR (2006)Google Scholar
  21. 21.
    Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait Recognition Using a View Transformation Model in the Frequency Domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151–163. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Lischinski, D.: Graphics gems iv, chapter incremental delaunay triangulation. Academic Press (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaobin Zhu
    • 1
  • Jing Liu
    • 1
  • Jinqiao Wang
    • 1
  • Wei Fu
    • 1
  • Hanqing Lu
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations