Reconstructing Motion Capture Data for Human Crowd Study

  • Samuel Lemercier
  • Mathieu Moreau
  • Mehdi Moussaïd
  • Guy Theraulaz
  • Stéphane Donikian
  • Julien Pettré
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7060)

Abstract

Reconstruction is a key step of the motion capture process. The quality of motion data first results from the quality of raw data. However, it also depends on the motion reconstruction step, especially when raw data suffer markers losses or noise due, for example, to challenging conditions of capture. Labeling is a final and crucial data reconstruction step that enables practical use of motion data (e.g., analysis). The lower the data quality, the more time consuming and tedious the labeling step, because human intervention cannot be avoided: he has to manually indicate markers label each time a loss of the marker in time occurs. In the context of crowd study, we faced such situation when we performed experiments on the locomotion of groups of people. Data reconstruction poses several problems such as markers labeling, interpolation and mean position computation. While Vicon IQ software has difficulties to automatically label markers for the crowd experiment we carried out, we propose a specific method to label our data and estimate participants mean positions with incomplete data.

Keywords

Motion Data Motion Capture Marker Position Marker Loss Pedestrian Behavior 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chai, J., Hodgins, J.K.: Performance animation from low-dimensional control signals. ACM Trans. Graph. 24, 686–696 (2005)CrossRefGoogle Scholar
  2. 2.
    Courty, N., Cuzol, A.: Conditional stochastic simulation for character animation. Computer Animation and Virtual Worlds 21, 443–452 (2010)Google Scholar
  3. 3.
    Daamen, W., Hoogendoorn, S.P.: Qualitative results from pedestrian laboratory experiments. In: Pedestrian and Evacuation Dynamics (2003)Google Scholar
  4. 4.
    Dorfmller-Ulhaas, K.: Robust optical user motion tracking using a kalman filter. Tech. rep., Universittsbibliothek der Universitt Augsburg (2003)Google Scholar
  5. 5.
    Herda, L., Fua, P., Plänkers, R., Boulic, R., Thalmann, D.: Skeleton-based motion capture for robust reconstruction of human motion. Comp. Animation, 77 (2000)Google Scholar
  6. 6.
    Kretz, T., Grnebohm, A., Schreckenberg, M.: Experimental study of pedestrian flow through a bottleneck. Journal of Statistical Mechanics: Theory and Experiment, 10014 (2006)Google Scholar
  7. 7.
    Li, L., McCann, J., Pollard, N., Faloutsos, C.: Bolero: a principled technique for including bone length constraints in motion capture occlusion filling. In: Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation (2010)Google Scholar
  8. 8.
    Pettré, J., Ondřej, J., Olivier, A.H., Cretual, A., Donikian, S.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation (2009)Google Scholar
  9. 9.
    Seyfried, A., Passon, O., Steffen, B., Boltes, M., Rupprecht, T., Klingsch, W.: New insights into pedestrian flow through bottlenecks. Transportation Science 43, 395–406 (2009)CrossRefGoogle Scholar
  10. 10.
    Still, G.: Crowd dynamics. Ph.D. thesis, University of Warwick, UK (2000)Google Scholar
  11. 11.
    Taylor, G.W., Hinton, G.E., Roweis, S.: Modeling human motion using binary latent variables. In: Advances in Neural Information Processing Systems (2006)Google Scholar
  12. 12.
    Yamori, K.: Going with the flow: Micro-macro dynamics in the macrobehavioral patterns of pedestrian crowds. Psychological Review 105, 530–557 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Samuel Lemercier
    • 1
  • Mathieu Moreau
    • 2
  • Mehdi Moussaïd
    • 2
  • Guy Theraulaz
    • 2
  • Stéphane Donikian
    • 1
    • 3
  • Julien Pettré
    • 1
  1. 1.INRIA Rennes - Bretagne AtlantiqueRennesFrance
  2. 2.CRCA, CNRSToulouse Cedex 9France
  3. 3.GolaemFrance

Personalised recommendations