Floor Fields for Tracking in High Density Crowd Scenes

  • Saad Ali
  • Mubarak Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)


This paper presents an algorithm for tracking individual targets in high density crowd scenes containing hundreds of people. Tracking in such a scene is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The novel tracking algorithm, which is outlined in this paper, will overcome these challenges using a scene structure based force model. In this force model an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in the scene. The key ingredients of the force model are three floor fields, which are inspired by the research in the field of evacuation dynamics, namely Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of move from one location to another by converting the long-range forces into local ones. The SFF specifies regions of the scene which are attractive in nature (e.g. an exit location). The DFF specifies the immediate behavior of the crowd in the vicinity of the individual being tracked. The BFF specifies influences exhibited by the barriers in the scene (e.g. walls, no-go areas). By combining cues from all three fields with the available appearance information, we track individual targets in high density crowds.


Particle Image Velocimetry Tracking Error Tracking Algorithm Track Length Tracking Failure 
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.
    Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of Pedestrian Dynamics Using a Two-dimensional Cellular Automation. Physica A 295(3) (2001)Google Scholar
  2. 2.
    Kirchner, A., et al.: Simulation of Evacuation Processes Using a Bionics-Inspired Cellular Automaton Model for Pedestrian Dynamics, vol. 312(1-2) (2002)Google Scholar
  3. 3.
    Lee, S.C., Hughes, R.L.: Exploring Trampling and Crushing in a Crowd. J. Transp. Engrg. 131(8) (2005)Google Scholar
  4. 4.
    Betke, M., et al.: Tracking Large Variable Numbers of Objects in Clutter. In: IEEE CVPR (2007)Google Scholar
  5. 5.
    Li, K., Kanade, T.: Cell Population Tracking and Lineage Construction Using Multiple-Model Dynamics Filters and Spatiotemporal Optimization. In: International Workshop on Microscopic Image Analysis with Applications in Biology (2007)Google Scholar
  6. 6.
    Khan, Z., Balch, T.R., Dellaert, F.: An MCMC-based Particle Filter for Tracking Multiple Interacting Targets. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 279–290. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Stauffer, C.: Estimating Tracking Sources and Sinks. In: IEEE Workshop on Event Mining (2003)Google Scholar
  8. 8.
    Gennari, G., Hager, G.D.: Probablistic Data Association Methods in Visual Tracking of Groups. In: IEEE CVPR (2004)Google Scholar
  9. 9.
    Cai, Y., et al.: Robust Visual Tracking of Multiple Targets. In: ECCV (2006)Google Scholar
  10. 10.
    Yilmaz, A., et al.: Object Tracking: A Survey. ACM Journal of Computing Surveys 38(4) (2006)Google Scholar
  11. 11.
    Ali, S., Shah, M.: A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis. IEEE CVPR (2007)Google Scholar
  12. 12.
    Brostow, G., Cipolla, R.: Unsupervised Bayesian Detection of Independent Motion in Crowds. In: IEEE CVPR (2006)Google Scholar
  13. 13.
    Zhao, T., Nevatia, R.: Bayesian Human Segmentation in Crowded Situations. In: IEEE CVPR (2003)Google Scholar
  14. 14.
    Zhao, T., Nevatia, R.: Tracking Multiple Humans in Crowded Environment. In: IEEE CVPR (2004)Google Scholar
  15. 15.
    Gurka, R., et al.: Computation of Pressure Distribution Using PIV Velocity Data. In: Proc. of the 3rd International Workshop on Particle Image Velocimetry (1999)Google Scholar
  16. 16.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. In: IEEE TPAMI (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Saad Ali
    • 1
  • Mubarak Shah
    • 1
  1. 1.Computer Vision LabUniversity of Central FloridaOrlandoUSA

Personalised recommendations