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Detection of Social Groups in Pedestrian Crowds Using Computer Vision

  • Sultan Daud Khan
  • Giuseppe Vizzari
  • Stefania Bandini
  • Saleh Basalamah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9386)

Abstract

We present a novel approach for automatic detection of social groups of pedestrians in crowds. Instead of computing pairwise similarity between pedestrian trajectories, followed by clustering of similar pedestrian trajectories into groups, we cluster pedestrians into a groups by considering only start (source) and stop (sink) locations of their trajectories. The paper presents the proposed approach and its evaluation using different datasets: experimental results demonstrate its effectiveness achieving significant accuracy both under dichotomous and trichotomous coding schemes. Experimental results also show that our approach is less computationally expensive than the current state-of-the-art methods.

Keywords

Group detection Hierarchical clustering Crowds analysis 

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References

  1. 1.
    Bandini, S., Gorrini, A., Vizzari, G.: Towards an integrated approach to crowd analysis and crowd synthesis: A case study and first results. Pattern Recognition Letters 44, 16–29 (2014)CrossRefGoogle Scholar
  2. 2.
    Bazzani, L., Cristani, M., Murino, V.: Decentralized particle filter for joint individual-group tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1886–1893. IEEE (2012)Google Scholar
  3. 3.
    Campbell, D.T.: Common fate, similarity, and other indices of the status of aggregates of persons as social entities. Behavioral Science 3(1), 14–25 (1958)CrossRefGoogle Scholar
  4. 4.
    Fu, Z., Hu, W., Tan, T.: Similarity based vehicle trajectory clustering and anomaly detection. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. II–602. IEEE (2005)Google Scholar
  5. 5.
    Ge, W., Collins, R.T., Ruback, R.B.: Vision-based analysis of small groups in pedestrian crowds. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(5), 1003–1016 (2012)CrossRefGoogle Scholar
  6. 6.
    Grimson, E., Wang, X., Ng, G.W., Ma, K.T.: Trajectory analysis and semantic region modeling using a nonparametric bayesian model (2008)Google Scholar
  7. 7.
    Hoogs, A., Perera, A.A.: Video activity recognition in the real world. In: AAAI, pp. 1551–1554 (2008)Google Scholar
  8. 8.
    Junejo, I.N., Javed, O., Shah, M.: Multi feature path modeling for video surveillance. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 716–719. IEEE (2004)Google Scholar
  9. 9.
    Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM (2000)Google Scholar
  10. 10.
    Khan, S., Vizzari, G., Bandini, S., Basalamah, S.: Detecting dominant motion flows and people counting in high density crowds. Journal of WSCG 22(1), 21–30 (2014)Google Scholar
  11. 11.
    Khan, S.D., Vizzari, G., Bandini, S.: Identifying sources and sinks and detecting dominant motion patterns in crowds. Transportation Research Procedia 2, 195–200 (2014)CrossRefGoogle Scholar
  12. 12.
    Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.: Everybody needs somebody: modeling social and grouping behavior on a linear programming multiple people tracker. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 120–127. IEEE (2011)Google Scholar
  13. 13.
    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1975–1981. IEEE (2010)Google Scholar
  14. 14.
    Mazzon, R., Poiesi, F., Cavallaro, A.: Detection and tracking of groups in crowd. In: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 202–207. IEEE (2013)Google Scholar
  15. 15.
    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
  16. 16.
    Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PloS One 5(4), e10047 (2010)CrossRefGoogle Scholar
  17. 17.
    Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 452–465. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  18. 18.
    Sankaranarayanan, K., Davis, J.W.: Learning directed intention-driven activities using co-clustering. In: AVSS, pp. 400–407 (2010)Google Scholar
  19. 19.
    Sochman, J., Hogg, D.C.: Who knows who-inverting the social force model for finding groups. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 830–837. IEEE (2011)Google Scholar
  20. 20.
    Solera, F., Calderara, S., Cucchiara, R.: Structured learning for detection of social groups in crowd. In: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 7–12. IEEE (2013)Google Scholar
  21. 21.
    Solmaz, B., Moore, B.E., Shah, M.: Identifying behaviors in crowd scenes using stability analysis for dynamical systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(10), 2064–2070 (2012)CrossRefGoogle Scholar
  22. 22.
    Vizzari, G., Manenti, L., Crociani, L.: Adaptive pedestrian behaviour for the preservation of group cohesion. Complex Adaptive Systems Modeling 1(1), 1–29 (2013)CrossRefGoogle Scholar
  23. 23.
    Wang, X., Ma, K.T., Ng, G.W., Grimson, W.E.L.: Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models. International Journal of Computer Vision 95(3), 287–312 (2011)CrossRefGoogle Scholar
  24. 24.
    Wang, X., Tieu, K., Grimson, E.: Learning semantic scene models by trajectory analysis. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 110–123. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  25. 25.
    Zamir, A.R., Dehghan, A., Shah, M.: GMCP-tracker: global multi-object tracking using generalized minimum clique graphs. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 343–356. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  26. 26.
    Zanotto, M., Bazzani, L., Cristani, M., Murino, V.: Online bayesian nonparametrics for group detection. In: Proceedings of British Machine Vision Conference, Surrey, p. 111–1 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sultan Daud Khan
    • 1
  • Giuseppe Vizzari
    • 1
  • Stefania Bandini
    • 1
  • Saleh Basalamah
    • 2
  1. 1.Complex Systems and Artificial Intelligence Research CentreUniversità degli Studi di Milano–BicoccaMilanoItaly
  2. 2.Department of Computer EngineeringUmm Al Qura UniversityMakkahSaudi Arabia

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