Predicting Pedestrian Trajectories

  • Stefano Pellegrini
  • Andreas Ess
  • Luc Van Gool


Pedestrians do not walk randomly. While they move toward their desired destination, they avoid static obstacles and other pedestrians. At the same time they try not to slow down too much as well as not to speed up excessively. Studies coming from the field of social psychology show that pedestrians exhibit common behavioral patterns. For example the distance at which one individual keeps himself from others is not uniformly random, but depends on the acquaintance level of the individuals, the culture and other factors. Our goal here is to use this knowledge to build a model that probabilistically represents the future state of a pedestrian trajectory. To this end, we focus on a stochastic motion model that caters for the possible behaviors in an entire scene in a multi-hypothesis approach, using a principled modeling of uncertainties.


Gaussian Mixture Model Motion Model Gibbs Measure World Model Static Obstacle 
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.
    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: ECCV (2008) Google Scholar
  2. 2.
    Antonini, G., Martinez, S.V., Bierlaire, M., Thiran, J.P.: Behavioral priors for detection and tracking of pedestrians in video sequences. Int. J. Comput. Vis. 69, 159–180 (2006) CrossRefGoogle Scholar
  3. 3.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50 (2002) Google Scholar
  4. 4.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2001). Software available at
  5. 5.
    Freedman, J.L.: Crowding and Behavior (1975) Google Scholar
  6. 6.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR (2006) Google Scholar
  7. 7.
    Hall, E.T.: The Hidden Dimension. Garden City (1966) Google Scholar
  8. 8.
    Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995) CrossRefGoogle Scholar
  9. 9.
    Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: EUROGRAPHICS (2007) Google Scholar
  10. 10.
    Luber, M., Stork, J.A., Tipaldi, G.D., Arras, K.O.: People tracking with human motion predictions from social forces. In: 2010 IEEE International Conference on Robotics and Automation, pp. 464–469. IEEE, New York (May 2010) CrossRefGoogle Scholar
  11. 11.
    Massive Software: Massive (2010) Google Scholar
  12. 12.
    McPhail, C., Wohlstein, R.T.: Using film to analyze pedestrian behavior. Sociol. Methods Res. 10(3), 347–375 (1982) CrossRefGoogle Scholar
  13. 13.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using Social Force model. In: CVPR (2009) Google Scholar
  14. 14.
    Pellegrini, S., Ess, A., Schindler, K., Gool, L.V.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: ICCV (2009) Google Scholar
  15. 15.
    Penn, A., Turner, A.: Space syntax based agent simulation. In: Pedestrian and Evacuation Dynamics (2002) Google Scholar
  16. 16.
    Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24(6), 843–854 (1979) CrossRefGoogle Scholar
  17. 17.
    Schadschneider, A.: Cellular automaton approach to pedestrian dynamics – theory. In: PED (2001) Google Scholar
  18. 18.
    Scovanner, P., Tappen, M.: Learning pedestrian dynamics from the real world. In: ICCV (2009) Google Scholar
  19. 19.
    Trautman, P., Krause, A.: Unfreezing the robot: Navigation in dense, interacting crowds. In: IROS (2010) Google Scholar
  20. 20.
    Ziebart, B.D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Andrew, J., Martial, B., Anind, H., Dey, K., Srinivasa, S.: Planning-based Prediction for Pedestrians (2009) Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Stefano Pellegrini
    • 1
  • Andreas Ess
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.ETH ZürichZürichSwitzerland
  2. 2.KU LeuvenLeuvenBelgium

Personalised recommendations