Going with the Flow: Pedestrian Efficiency in Crowded Scenes

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)


Video analysis of crowded scenes is challenging due to the complex motion of individual people in the scene. The collective motion of pedestrians form a crowd flow, but individuals often largely deviate from it as they anticipate and react to each other. Deviations from the crowd decreases the pedestrian’s efficiency: a sociological concept that measures the difference of actual motion from the intended speed and direction. In this paper, we derive a novel method for estimating pedestrian efficiency from videos. We first introduce a novel crowd motion model that encodes the temporal evolution of local motion patterns represented with directional statistics distributions. This model is then used to estimate the intended motion of pedestrians at every space-time location, which enables visual measurement of the pedestrian efficiency. We demonstrate the use of this pedestrian efficiency to detect unusual events and to track individuals in crowded scenes. Experimental results show that the use of pedestrian efficiency leads to state-of-the-art accuracy in these critical applications.


Anomaly Detection Emergent Behavior Intended Motion Social Force Model Crowded Scene 
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.


  1. 1.
    Helbing, D., Vicsek, T.: Optimal Self-Organization. New Journal of Physics 13 (1999)Google Scholar
  2. 2.
    Helbing, D., Moln, P., Farkas, I.J., Bolay, K.: Self-Organizing Pedestrian Movement. Environment and Planning B: Planning and Design 28, 361–383 (2001)CrossRefGoogle Scholar
  3. 3.
    Still, K.: Crowd Dynamics. PhD thesis, University of Warwick (2000)Google Scholar
  4. 4.
    Schadschneider, A., Klingsch, W., Kluepfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Evacuation Dynamics: Empirical Results, Modeling and Applications. In: Encyclopedia of Complexity and Systems Science, pp. 3142–3176 (2009)Google Scholar
  5. 5.
    Moore, B.E., Ali, S., Mehran, R., Shah, M.: Visual Crowd Surveillance Through a Hydrodynamics Lens. Comm. of ACM 54, 64–73 (2011)CrossRefGoogle Scholar
  6. 6.
    Mehran, R., Oyama, A., Shah, M.: Abnormal Crowd Behavior Detection using Social Force Model. In: Proc. of IEEE CVPR (2009)Google Scholar
  7. 7.
    Krausz, B., Bauckhage, C.: Analyzing Pedestrian Behavior in Crowds for Automatic Detection of Congestions. In: Proc. of IEEE Workshop on MSVLC (2011)Google Scholar
  8. 8.
    Hoogendoorn, S.P., Daamen, W.: Pedestrian Behavior at Bottlenecks. Transportation Science 39 (2005)Google Scholar
  9. 9.
    Kratz, L., Nishino, K.: Anomaly Detection in Extremely Crowded Scenes Using Spatio-Temporal Motion Pattern Models. In: Proc. of IEEE CVPR, pp. 1446–1453 (2009)Google Scholar
  10. 10.
    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly Detection in Crowded Scenes. In: Proc. of IEEE CVPR, pp. 1975–1981 (2010)Google Scholar
  11. 11.
    Rodriguez, M., Ali, S., Kanade, T.: Tracking in Unstructured Crowded Scenes. In: Proc. of IEEE ICCV (2009)Google Scholar
  12. 12.
    Kratz, L., Nishino, K.: Tracking Pedestrians using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes. IEEE TPAMI 34, 987–1002 (2012)CrossRefGoogle Scholar
  13. 13.
    Ali, S., Shah, M.: Floor Fields for Tracking in High Density Crowd Scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 1–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Yu, Q., Medioni, G.: Motion pattern interpretation and detection for tracking moving vehicles in airborne video. In: Proc. of IEEE CVPR, pp. 2671–2678 (2009)Google Scholar
  15. 15.
    Kretz, T., Grunebohm, A., Kaufman, M., Mazur, F., Schreckenberg, M.: Experimental Study of Pedestrian Counterflow in a Corridor. JSTAT 2006, P10001 (2006)Google Scholar
  16. 16.
    Wright, J., Pless, R.: Analysis of Persistent Motion Patterns Using the 3D Structure Tensor. In: IEEE WACV, pp. 14–19 (2005)Google Scholar
  17. 17.
    Brox, T., Malik, J.: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. IEEE TPAMI 33, 500–513 (2011)CrossRefGoogle Scholar
  18. 18.
    Mardia, K.V., Jupp, P.: Directional Statistics. John Wiley and Sons Ltd. (1999)Google Scholar
  19. 19.
    Mardia, A., El-Atoum, S.: Bayesian Inference for The Von Mises-Fisher Distribution Miscellanea. Biometrika 63, 203–206 (1976)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2007)Google Scholar
  21. 21.
    Zipf, G.: Human Behavior and the Principle of Least Effort. Addison-Wesley Press (1949)Google Scholar
  22. 22.
    Guy, S., Chhugani, J., Curtis, S., Dubey, P., Lin, M., Manocha, D.: PLEdestrians: A Least-Effort Approach to Crowd Simulation. In: Proc. of ACM/EG SCA, pp. 119–128 (2010)Google Scholar
  23. 23.
    Teknomo, K.: Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model. PhD thesis, Tohoku University (2002)Google Scholar
  24. 24.
    Henderson, L.F.: The Statistics of Crowd Fluids. Nature 229 (1971)Google Scholar
  25. 25.
    Chauvenet, W.: In: A Manual of Spherical and Practical Astronomy, 5th edn., pp. 474–566. Adamant Media Corporation (1891)Google Scholar
  26. 26.
    Boykov, Y., Kolmogorov, V.: An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE TPAMI 26, 1124–1137 (2004)CrossRefGoogle Scholar
  27. 27.
    Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A Comparative Study of Energy Minimization Methods for Markov Random Fields. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 16–29. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  28. 28.
    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  29. 29.
    Isard, M., Blake, A.: CONDENSATION-Conditional Density Propagation for Visual Tracking. IJCV 29, 5–28 (1998)CrossRefGoogle Scholar
  30. 30.
    Ali, S., Shah, M.: A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis. In: Proc. of IEEE CVPR, pp. 1–6 (2007)Google Scholar
  31. 31.
    Cheriyadat, A., Radke, R.: Detecting Dominant Motions in Dense Crowds. IEEE Journal of Selected Topics in Signal Processing 2, 568–581 (2008)CrossRefGoogle Scholar
  32. 32.
    University of Minnesota: Unusual Crowd Activity Dataset (2006),
  33. 33.
    Raghavendra, R., Bue, A.D., Cristani, M., Murino, V.: Optimizing Interaction Force for Global Anomaly Detection in Crowded Scenes. In: Proc. of IEEE ICCV, pp. 136–143 (2011)Google Scholar
  34. 34.
    University of California San Diego: Anomaly Detection Dataset (2010),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceDrexel UniversityPhiladelphiaUSA

Personalised recommendations