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Long Term Real Trajectory Reuse through Region Goal Satisfaction

  • Junghyun Ahn
  • Stéphane Gobron
  • Quentin Silvestre
  • Horesh Ben Shitrit
  • Mirko Raca
  • Julien Pettré
  • Daniel Thalmann
  • Pascal Fua
  • Ronan Boulic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7060)

Abstract

This paper is motivated by the objective of improving the realism of real-time simulated crowds by reducing short term collision avoidance through long term anticipation of pedestrian trajectories. For this aim, we choose to reuse outdoor pedestrian trajectories obtained with non-invasive means. This initial step is achieved by analyzing the recordings of multiple synchronized video cameras. In a second off-line stage, we fit as long as possible trajectory segments within predefined paths made of a succession of region goals. The concept of region goal is exploited to enforce the principle of “sufficient satisfaction”: it allows the pedestrians to relax the prescribed trajectory to the traversal of successive region goals. However, even if a fitted trajectory is modified due to collision avoidance, we are still able to make long-term trajectory anticipation and distribute the collision avoidance shift over a long distance.

Keywords

Motion trajectories Collision handling 

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References

  1. 1.
    Berclaz, J., Fleuret, F., Türetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. PAMI (February 2011)Google Scholar
  2. 2.
    Boulic, R.: Relaxed Steering Towards Oriented Region Goals. In: Egges, A., Kamphuis, A., Overmars, M. (eds.) MIG 2008. LNCS, vol. 5277, pp. 176–187. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 1820–1833 (2011)CrossRefGoogle Scholar
  4. 4.
    Brogan, D.C., Johnson, N.L.: Realistic human walking paths. In: CASA 2003, pp. 94–101 (2003)Google Scholar
  5. 5.
    Chenney, S.: Flow tiles. In: SCA 2004, pp. 233–242 (2004)Google Scholar
  6. 6.
    Courty, N., Corpetti, T.: Crowd motion capture. Computer Animation and Virtual Worlds 18, 361–370 (2007)CrossRefGoogle Scholar
  7. 7.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-camera people tracking with a probabilistic occupancy map. PAMI 30(2), 267–282 (2008)CrossRefGoogle Scholar
  8. 8.
    Hillier, B., Penn, A., Hanson, J., Grajewski, T., Xu, J.: Natural movement: or, configuration and attraction in urban pedestrian movement. Environment and Planning B: Planning and Design 20(1), 29–66 (1993)CrossRefGoogle Scholar
  9. 9.
    Kapadia, M., Singh, S., Hewlett, W., Faloutsos, P.: Egocentric affordance fields in pedestrian steering. In: I3D 2009, pp. 215–223. ACM (2009)Google Scholar
  10. 10.
    Karamouzas, I., Heil, P., van Beek, P., Overmars, M.H.: A Predictive Collision Avoidance Model for Pedestrian Simulation. In: Egges, A., Geraerts, R., Overmars, M. (eds.) MIG 2009. LNCS, vol. 5884, pp. 41–52. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Karamouzas, I., Overmars, M.: Simulating the local behaviour of small pedestrian groups. In: VRST 2010, pp. 183–190. ACM (2010)Google Scholar
  12. 12.
    Kwon, T., Lee, K.H., Lee, J., Takahashi, S.: Group motion editing. ACM Transactions on Graphics 27, 80:1–80:8 (2008)CrossRefGoogle Scholar
  13. 13.
    Lee, K.H., Choi, M.G., Hong, Q., Lee, J.: Group behavior from video: a data-driven approach to crowd simulation. In: SCA 2007, pp. 109–118 (2007)Google Scholar
  14. 14.
    Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. Computer Graphics Forum 26(3), 655–664 (2007)CrossRefGoogle Scholar
  15. 15.
    Metoyer, R.A., Hodgins, J.K.: Reactive pedestrian path following from examples. In: CASA 2003. IEEE Computer Society (2003)Google Scholar
  16. 16.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A bayesian computer vision system for modeling human interactions. PAMI 22(8), 831–843 (2000)CrossRefGoogle Scholar
  17. 17.
    Park, M.J.: Guiding flows for controlling crowds. Vis. Comput. 26, 1383–1391 (2010)CrossRefGoogle Scholar
  18. 18.
    Pettré, J.: Populate Your Game Scene. In: Egges, A., Kamphuis, A., Overmars, M. (eds.) MIG 2008. LNCS, vol. 5277, pp. 33–42. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Reitsma, P.S.A., Pollard, N.S.: Evaluating motion graphs for character animation. ACM Trans. Graph. 26 (October 2007)Google Scholar
  20. 20.
    Simon, H.A.: Rational choice and the structure of the environment. Psychological Review 63(2), 129–138 (1956)CrossRefGoogle Scholar
  21. 21.
    Stylianou, S., Fyrillas, M.M., Chrysanthou, Y.: Scalable pedestrian simulation for virtual cities. In: VRST 2004, pp. 65–72. ACM (2004)Google Scholar
  22. 22.
    Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. ACM Trans. Graph. 25, 1160–1168 (2006)CrossRefGoogle Scholar
  23. 23.
    Tsai, R.Y.: A versatile cameras calibration technique for high accuracy 3d machine vision mtrology using off-the-shelf tv cameras and lenses. JRA 3(4), 323–344 (1987)Google Scholar
  24. 24.
    Tversky, A., Kahneman, D.: Judgment under uncertainty: Heuristics and biases. Science 185(4157), 1124–1131 (1974)CrossRefGoogle Scholar
  25. 25.
    van den Akker, M., Geraerts, R., Hoogeveen, H., Prins, C.: Path planning for groups using column generation. In: Boulic, R., Chrysanthou, Y., Komura, T. (eds.) MIG 2010. LNCS, vol. 6459, pp. 94–105. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    Yersin, B., Maïm, J., Morini, F., Thalmann, D.: Real-time crowd motion planning: Scalable avoidance and group behavior. Vis. Comput. 24, 859–870 (2008)CrossRefGoogle Scholar
  27. 27.
    Yersin, B., Maïm, J., Pettré, J., Thalmann, D.: Crowd patches: populating large-scale virtual environments for real-time applications. In: I3D, pp. 207–214 (2009)Google Scholar
  28. 28.
    Zipf, G.: Human Behaviour and the Principle of Least-Effort. Addison-Wesley, Cambridge (1949)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Junghyun Ahn
    • 1
  • Stéphane Gobron
    • 1
  • Quentin Silvestre
    • 1
  • Horesh Ben Shitrit
    • 1
  • Mirko Raca
    • 1
  • Julien Pettré
    • 2
  • Daniel Thalmann
    • 3
  • Pascal Fua
    • 1
  • Ronan Boulic
    • 1
  1. 1.EPFLSwitzerland
  2. 2.INRIA-RennesFrance
  3. 3.NTUSingapore

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