A Fuzzy Logic Based Approach for Crowd Simulation

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 149)

Abstract

In this article, we present a new approach which incorporates fuzzy logic with data-driven crowd simulation method. Behavior rules are derived from the state-action samples obtained from crowd videos by MLFE algorithm. The derived rules complement the disadvantages of rule-based approach in the situation where the crowd behavior can’t be reduced to rules, i.e., calibrate the behaviors produced by rule-based approach. During a simulation, the new derived rules can be combined with pre-defined ones. Then, the compositive rules are treated in the overview of fuzzy logic to simulate various crowd behaviors. It is noticeable that the derived rules can be used independently for data-driven crowd simulation or be incorporated with predefined rules. The advantages of our approach are obvious: interoperability, universality and behavior’s diversity.

Keywords

crowd simulation fuzzy logic MLFE rule extraction 

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References

  1. 1.
    Lee, K.H., Choi, M.G., Hong, Q., Lee, J.: Group Behavior from Video: A Data-Driven Approach to Crowd Simulation. In: Proc. ACM SIGGRAPH/ Eurographics Symposium on Computer Animation, San Diego, Eurographics Association Aire-la-Ville, Switzerland, pp. 109–118 (2007)Google Scholar
  2. 2.
    Zhou, S., Chen, D., Cai, W.: Crowd modeling and simulation technologies. ACM Transactions on Modeling and Computer Simulation 20(4), 1–39 (2009)CrossRefGoogle Scholar
  3. 3.
    Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by Example. Conputer Graphics Forum 26(3), 655–664 (2007)CrossRefGoogle Scholar
  4. 4.
    Magnenat-Thalmann, N., Thalmann, D.: Virtual Humans: Thirty Years of Research, What Next? The Visual Computer 21(12), 997–1015 (2005)CrossRefGoogle Scholar
  5. 5.
    Ross, T.J.: Fuzzy logic with engineering application, 3rd edn. John Wiley & Sons, Ltd. (2010)Google Scholar
  6. 6.
    Ghasem-Aghaee, N., Ören, T.I.: Towards Fuzzy Agents with Dynamic Personality for Human Behavior Simulation. In: Proceedings of the 2003 Summer Computer Simulation Conference, Montreal, PQ, Canada, pp. 3–10 (2003)Google Scholar
  7. 7.
    Thalmann, D., Raupp Musse, S.: Crowd Simulation. Springer, London (2007)Google Scholar
  8. 8.
    Pelechano, N., Allbeck, J., Badler, N.I.: Virtual Crowds: Methods, Simulation, and Control. Morgan&Claypool Publishers (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of Mechanical Engineering and AutomationNational University of Defence TechnologyChangshaP.R. China
  2. 2.Department of Information Security, College of Electronic EngineeringNaval University of EngineeringWuhanP.R. China

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