Advertisement

Machine Vision and Applications

, Volume 30, Issue 5, pp 919–931 | Cite as

Statistical detection of a panic behavior in crowded scenes

  • Doaa Shehab
  • Heyfa AmmarEmail author
Special Issue Paper

Abstract

Crowd scenes analysis is becoming one of the most active researches in computer vision. Panic behavior is a key indication of the occurrence of an abnormal event within the human crowd, and its detection helps preventing disastrous situations. The detection techniques reported in the literature analyze the temporal variation of either the motion magnitudes, the motion orientations, the crowd density or people interactions. However, all these features contribute to the characterization of a crowd behavior and ignoring one of them may lead to the degradation of the detection performances. In the present work, our contribution is threefold. First, a novel feature is proposed. It allows to simultaneously take into consideration all the aforementioned characteristics in order to analyze the human crowd. Second, a sparse representation is proposed and aims to facilitate the distinction between non-panic and panic situations. Third, data related to a panic behavior are considered as outliers with respect to non-panic related data and are statistically detected. The approach proposed in the present study has four major advantages. First, it does not depend on the crowd density level. Second, its detection performances outperform the state-of-the-art techniques for most of the videos. Third, it is not restricted to specific panic behaviors like escaping, gathering, dispersion and so on; it is applicable to any of the panic behaviors. Fourth, it is simple and easy to implement.

Keywords

Panic Motion Gradient Outliers Abnormal 

Notes

Acknowledgements

This research was supported by King Abdulaziz City for Science and Technology (Grant No. PS-38-2006). We thank them for their support.

Supplementary material

Supplementary material 1 (mp4 21789 KB)

References

  1. 1.
    T.O.S.P. Agency. Hajj disasters. http://www.spa.gov.sa/. Accessed April 2016
  2. 2.
    Kok, V.J., Lim, M.K., Chan, C.S.: Crowd behavior analysis: a review where physics meets biology. Neurocomputing 177, 342–362 (2016)CrossRefGoogle Scholar
  3. 3.
    The Guardian: Saudi Arabia’s latest hajj disaster raises serious safety questions. https://www.theguardian.com/world/2015/sep/24/saudi-arabia-latest-hajj-disaster-serious-safety-questions. Accessed Sept 2018
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Sjarif, N., Shamsuddin, S., Hashim, S.: Detection of abnormal behaviors in crowd scene: a review. Int. J. Adv. Soft Comput. Appl. 4(1), 1–33 (2012)Google Scholar
  6. 6.
    Cooks, T.: Factors affecting emergency manager, first responder, and citizen disaster preparedness. Ph.D. dissertation, Walden University (2015)Google Scholar
  7. 7.
    Morganstein, J.C., West, J.C., Huff, L.A., Flynn, B.W., Fullerton, C.S., Benedek, D.M., Ursano, R.J.: Psychosocial responses to disaster and exposures: distress reactions, health risk behavior, and mental disorders. In: Shigemura, J., Chhem, R. (eds.) Mental Health and Social Issues Following a Nuclear Accident, pp. 99–117. Springer, Berlin (2016)CrossRefGoogle Scholar
  8. 8.
    Gnanavel, V., Srinivasan, A.: Abnormal event detection in crowded video scenes. In: FICTA (2), pp. 441–448 (2014)Google Scholar
  9. 9.
    Pennisi, A., Bloisi, D.D., Iocchi, L.: Online real-time crowd behavior detection in video sequences. Comput. Vis. Image Underst. 144, 166–176 (2016)CrossRefGoogle Scholar
  10. 10.
    Liu, Y., Li, X., Jia, L.: Abnormal crowd behavior detection based on optical flow and dynamic threshold. In: 2014 11th World Congress on Intelligent Control and Automation (WCICA), pp. 2902–2906. IEEE (2014)Google Scholar
  11. 11.
    Li, A., Miao, Z., Cen, Y., Wang, T., Voronin, V.: Histogram of maximal optical flow projection for abnormal events detection in crowded scenes. Int. J. Distrib. Sens. Netw. 11, 406941 (2015)CrossRefGoogle Scholar
  12. 12.
    Cong, Y., Yuan, J., Liu, J.: Abnormal event detection in crowded scenes using sparse representation. Pattern Recognit. 46(7), 1851–1864 (2013)CrossRefGoogle Scholar
  13. 13.
    Kumar, A.: Panic detection in human crowds using sparse coding. Master’s thesis, University of Waterloo (2012)Google Scholar
  14. 14.
    Chen Chunyu, X.B., Shao, Yu.: Detection of anomalous crowd behavior based on the acceleration feature. IEEE Sens. J. 15(12), 7252–7261 (2015)CrossRefGoogle Scholar
  15. 15.
    Chen, C.-Y., Shao, Y.: Crowd escape behavior detection and localization based on divergent centers. IEEE Sens. J. 15(4), 2431–2439 (2015)CrossRefGoogle Scholar
  16. 16.
    Direkoglu, C., Sah, M., O’Connor, N.E.: Abnormal crowd behavior detection using novel optical flow-based features (2017)Google Scholar
  17. 17.
    Wu, S., Wong, H.-S., Yu, Z.: A bayesian model for crowd escape behavior detection. IEEE Trans. Circuits Syst. Video Technol. 24(1), 85–98 (2014)CrossRefGoogle Scholar
  18. 18.
    Mousavi, H., Mohammadi, S., Perina, A., Chellali, R., Murino, V.: Analyzing tracklets for the detection of abnormal crowd behavior. In: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 148–155 (2015)Google Scholar
  19. 19.
    Fradi, H., Dugelay, J.-L.: Towards crowd density-aware video surveillance applications. Inf. Fusion 24, 3–15 (2015)CrossRefGoogle Scholar
  20. 20.
    Gunduz, A.E., Ongun, C., Temizel, T.T., Temizel, A.: Density aware anomaly detection in crowded scenes. IET Comput. Vis. 10(5), 374–381 (2016)CrossRefGoogle Scholar
  21. 21.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 935–942. IEEE (2009)Google Scholar
  22. 22.
    Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2014)CrossRefGoogle Scholar
  23. 23.
    Hu, X., Hu, S., Zhang, X., Zhang, H., Luo, L.: Anomaly detection based on local nearest neighbor distance descriptor in crowded scenes. Sci. World J. 2014, 1–12 (2014)Google Scholar
  24. 24.
    Sharbini, H.B., Bade, A.: Analysis of crowd behaviour theories in panic situation. In: International Conference on Information and Multimedia Technology, 2009. ICIMT’09, pp. 371–375. IEEE (2009)Google Scholar
  25. 25.
    Huang, K., Aviyente, S.: Sparse representation for signal classification. In: Advances in Neural Information Processing Systems, pp. 609–616 (2007)Google Scholar
  26. 26.
    de Almeida, I.R., Cassol, V.J., Badler, N.I., Musse, S.R., Jung, C.R.: Detection of global and local motion changes in human crowds. IEEE Trans. Circuits Syst. Video Technol. 27(3), 603–612 (2017)CrossRefGoogle Scholar
  27. 27.
    Ali, S., Shah, M.: A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp. 1–6. IEEE (2007)Google Scholar
  28. 28.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)CrossRefGoogle Scholar
  29. 29.
    Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1–3), 185–203 (1981)CrossRefGoogle Scholar
  30. 30.
    Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439. IEEE (2010)Google Scholar
  31. 31.
    Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vis. Image Underst. 63(1), 75–104 (1996)CrossRefGoogle Scholar
  32. 32.
    University of Minnesota: Unusual crowd activity dataset. http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi. Accessed Nov 2016
  33. 33.
    Neykov, N., Filzmoser, P., Dimova, R., Neytchev, P.: Robust fitting of mixtures using the trimmed likelihood estimator. Comput. Stat. Data Anal. 52(1), 299–308 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Hadi, A.S., Luceño, A.: Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms. Comput. Stat. Data Anal. 25(3), 251–272 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  35. 35.
    Rousseeuw, P.J., Driessen, K.V.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3), 212–223 (1999)CrossRefGoogle Scholar
  36. 36.
    Ferryman, A.J.: Pets2009benchmarkdata. http://cs.binghamton.edu/mrldata/pets2009.html. Accessed Feb 2017
  37. 37.
    Allain, P., Courty, T.N., Creusot, C.: Agoraset: a dataset for crowd video analysis. http://www.sites.univ-rennes2.fr/costel/corpetti/agoraset/Site/AGORASET.html. Accessed Feb 2017
  38. 38.
    Wazee Digital: Cloud digital asset management platform. http://commerce.wazeedigital.com/license/clip/14121797.do. Accessed Sept 2017
  39. 39.
    Wazee Digital: Cloud digital asset management platform. http://commerce.wazeedigital.com/license/clip/3682865.do. Accessed Sept 2017
  40. 40.
    de Almeida, I.R., Jung, C.R.: Change detection in human crowds. In: 2013 26th SIBGRAPI-Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, pp. 63–69 (2013)Google Scholar
  41. 41.
    Chen, D.-Y., Huang, P.-C.: Motion-based unusual event detection in human crowds. J. Vis. Commun. Image Represent. 22(2), 178–186 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.FCITKing Abdulaziz University, KSAJeddahSaudi Arabia
  2. 2.University of Tunis El ManarTunisTunisia

Personalised recommendations