Skip to main content
SpringerLink
Log in
Book cover

International Conference on Articulated Motion and Deformable Objects

AMDO 2016: Articulated Motion and Deformable Objects pp 66–80Cite as

Realistic Crowds via Motion Capture and Cell Marking

  • Conference paper
  • First Online:

  • 913 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9756))

Abstract

Ever since the first use of crowds in films and video games there has been an interest in larger, more efficient and more realistic simulations of crowds. Most crowd simulation algorithms are able to satisfy the viewer from a distance but when inspected from close up the flaws in the individual agent’s movements become noticeable. One of the bigger challenges faced in crowd simulation is finding a solution that models the actual movement of an individual in a crowd. This paper simulates a more realistic crowd by using individual motion capture data as well as traditional crowd control techniques. By augmenting traditional crowd control algorithms with the use of motion capture data for individual agents, we can simulate crowds that mimic more realistic crowd motion, while maintaining real-time simulation speed.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Reynolds, C.: Flocks, herds and schools: a distributed behavioral model. In: ACM SIGGRAPH Computer Graphics, vol. 21, pp. 25–34. ACM (1987)

    Google Scholar 

  2. Massive Software (2015). http://massivesoftware.com

  3. Golaem Software (2015). http://golaem.com

  4. Amkraut, S., Girard, M., Karl, G.: Eurythmy. SIGGRAPH Video Rev. 21, 329–336 (1985)

    Google Scholar 

  5. Reynolds, C.: Steering behaviors for autonomous characters. In: Game Developers Conference (1999). http://wwwred3d.com/cwr/steer/gdc99

  6. Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using the relative velocity paradigm. In: Proceedings of 1993 IEEE International Conference on Robotics and Automation, pp. 560–565. IEEE (1993)

    Google Scholar 

  7. Van den Berg, J., Lin, M., Minocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: ICRA 2008, IEEE International Conference on Robotics and Automation, pp. 1928–1935. IEEE (2008)

    Google Scholar 

  8. Xiong, M., Lees, M., Cai, W., Zhou, S., Low, M.Y.H.: A rule-based motion planning for crowd simulation. In: CW 2009, International Conference on CyberWorlds 2009, pp. 88–95. IEEE (2009)

    Google Scholar 

  9. Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)

    Article  Google Scholar 

  10. Braun, A., Musse, S.R., De Oliveira, L.P., Bodman, B.E.: Modeling individual behaviors in crowd simulation. In: 16th International Conference on Computer Animation and Social Agents, 2003, pp. 143–148. IEEE (2003)

    Google Scholar 

  11. Pelechano, N., Allbeck, J., Badler, N.: Controlling individual agents in high-density crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 99–108. Eurographics Association (2007)

    Google Scholar 

  12. Ricks, B.C., Egbert, P.K.: More realistic, flexible, and expressive social crowds using transactional analysis. Vis. Comput. 28(6–8), 889–898 (2012)

    Article  Google Scholar 

  13. Kim, S., Guy, S.J., Manocha, D., Lin, M.C.: Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, pp. 55–62. ACM (2012)

    Google Scholar 

  14. Blue, V.J., Adler, J.L.: Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transp. Res. Part B: Methodological 35(3), 293–312 (2001)

    Article  Google Scholar 

  15. Schadschneider, A.: Cellular automaton approach to pedestrian dynamics-theory. arXiv preprint cond-mat/0112117 (2001)

    Google Scholar 

  16. Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys. A 295(3), 507–525 (2001)

    Article  MATH  Google Scholar 

  17. Hamagami, T., Hirata, H.: Method of crowd simulation by using multiagent on cellular automata. In: IAT 2003, IEEE/WIC International Conference on Intelligent Agent Technology, pp. 46–52. IEEE (2003)

    Google Scholar 

  18. Chenney, S.: Flow tiles. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation, Eurographics Association, pp. 233–242 (2004)

    Google Scholar 

  19. Tecchia, F., Loscos, C., Conroy-Dalton, R., Chrysanthou, Y.L.: Agent behaviour simulator (abs): A platform for urban behaviour development (2001)

    Google Scholar 

  20. Musse, S.R., Jung, C.R., Jacques, J., Braun, A.: Using computer vision to simulate the motion of virtual agents. Comput. Animation Virtual Worlds 18(2), 83–93 (2007)

    Article  Google Scholar 

  21. Zhong, J., Cai, W., Luo, L., Yin, H.: Learning behavior patterns from video: a data-driven framework for agent-based crowd modeling. In: Proceedings of the AAMAS (2015)

    Google Scholar 

  22. Ahn, J., et al.: Long term real trajectory reuse through region goal satisfaction. In: Allbeck, J.M., Faloutsos, P. (eds.) MIG 2011. LNCS, vol. 7060, pp. 412–423. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Lee, K., Choi, M., Hong, Q., Lee, J.: Group behavior from video: a data-driven approach to crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 109–118. Eurographics Association (2007)

    Google Scholar 

  24. Courty, N., Corpetti, T.: Crowd motion capture. Comput. Animation Virtual Worlds 18(4–5), 361–370 (2007)

    Article  Google Scholar 

  25. Pelechano, N., Spanlang, B., Beacco, A.: Avatar locomotion in crowd simulation. In: International Conference on Computer Animation and Social Agents (CASA) (2011)

    Google Scholar 

  26. Kim, J., Seol, Y., Kwon, T., Lee, J.: Interactive manipulation of large-scale crowd animation, ACM Trans. Graph. 33(4), Article 83 (2014)

    Google Scholar 

  27. Lee, K.H., Choi, M.G., Lee, J.: Motion patches: building blocks for virtual environments annotated with motion data, In: ACM SIGGRAPH 2006 Papers (SIGGRAPH 2006), pp. 898–906. ACM, New York (2006)

    Google Scholar 

  28. Lemercier, S., Moreau, M., Moussaïd, M., Theraulaz, G., Donikian, S., Pettré, J.: Reconstructing motion capture data for human crowd study. In: Allbeck, J.M., Faloutsos, P. (eds.) MIG 2011. LNCS, vol. 7060, pp. 365–376. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  29. Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer Graphics Forum, vol. 26, pp. 655–664. Wiley Online Library (2007)

    Google Scholar 

  30. Carnegie Mellon University. CMU graphics lab motion capture database. http://www.mocap.cs.cmu.edu

  31. Egbert, C., Egbert, P.K., Morse, B.S.: Real-time motion transition by example. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2010. LNCS, vol. 6169, pp. 138–147. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  32. Kovar, L., Gleicher, M., Pighin, F.: Motion graphs. In: ACM SIGGRAPH 2008 Classes, p. 51. ACM (2008)

    Google Scholar 

  33. Ondrej, J., Pettre, J., Olivier, A.H., Donikian, S.: A synthetic-vision based steering approach for crowd simulation. ACM Trans. Graph. (TOG) 29(4), 123 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Brigham Young University, Provo, UT, USA

    Seth Brunner & Parris K. Egbert

  2. University of Nebraska at Omaha, Omaha, NE, USA

    Brian Ricks

Authors
  1. Seth Brunner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian Ricks
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parris K. Egbert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Parris K. Egbert .

Editor information

Editors and Affiliations

  1. UIB-Universitat de les Illes Balears, Palma de Mallorca, Spain

    Francisco José Perales

  2. University of Surrey, Guildford, United Kingdom

    Josef Kittler

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Brunner, S., Ricks, B., Egbert, P.K. (2016). Realistic Crowds via Motion Capture and Cell Marking. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41778-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41777-6

  • Online ISBN: 978-3-319-41778-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation