Modeling and Analyzing the Human Cognitive Limits for Perception in Crowd Simulation

  • Vaisagh Viswanathan
  • Michael Lees
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7380)

Abstract

One of the major components of Agent Based Crowd Simulation is motion planning. There have been various motion planning algorithms developed and they’ve become increasingly better and more efficient at calculating the most optimal path. We believe that this optimality is coming at the price of realism. Certain factors like social norms, limitations to human computation capabilities, etc. prevent humans from following their optimal path. One aspect of natural movement is related to perception and the manner in which humans process information. In this paper we propose two additions to general motion planning algorithms: (1) Group sensing for motion planning which results in agents avoiding clusters of other agents when choosing their collision free path. (2) Filtering of percepts based on interestingness to model limited information processing capabilities of human beings.

Keywords

Agent-Based Model Sensing Crowd Simulation Motion Planning Visual Cognition Group Based Perception Information Collision Avoidance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van den Berg, J., Patil, S., Sewall, J., Manocha, D., Lin, M.: Interactive navigation of multiple agents in crowded environments. In: 2008 Symposium on Interactive 3D Graphics and Games. University of North Carolina (2008)Google Scholar
  2. 2.
    Bonabeau, E.: Agent-based modeling methods and techniques for simulating human systems. In: Arthur M. Sackier Colloquium of the National Academy of Sciences, pp. 7280–7287. Icosystem Corporation, 545 Concord Avenue (2002)Google Scholar
  3. 3.
    Broadbent, D.E.: Applications of Information Theory and Decision Theory to Human Perception and Reaction. Progress in Brain Research 17, 309–320 (1965)CrossRefGoogle Scholar
  4. 4.
    Courty, N., Marchand, E., Arnaldi, B.: A New Application for Saliency Maps: Synthetic Vision of Autonomous Actors. In: Proceedings of the 2003 International Conference on Image Processing, ICIP 2003 (2003)Google Scholar
  5. 5.
    Cowan, N.: The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences 24(01), 87–114 (2001)CrossRefGoogle Scholar
  6. 6.
    Epstein, J.M.: Agent-based computational models and generative social science. Complexity 4(5), 41–60 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Grillon, H., Thalmann, D.: Simulating gaze attention behaviors for crowds. Computer Animation and Virtual Worlds 20(2-3), 111–119 (2009)CrossRefGoogle Scholar
  8. 8.
    Guy, S., van den Berg, J., Lin, M.: Geometric methods for multi-agent collision avoidance. In: Proceedings of the 2010 Annual Symposium on Computational Geometry, pp. 115–116. University of North Carolina, Utah (2010)CrossRefGoogle Scholar
  9. 9.
    Guy, S.J., Chhugani, J., Curtis, S., Dubey, P., Lin, M., Manocha, D.: PLEdestrians: a least-effort approach to crowd simulation. In: SCA 2010: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Eurographics Association, University of North Carolina (July 2010)Google Scholar
  10. 10.
    Guy, S.J., Chhugani, J., Kim, C., Satish, N., Lin, M., Manocha, D., Dubey, P.: ClearPath: highly parallel collision avoidance for multi-agent simulation. In: SCA 2009: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. ACM Request Permissions (August 2009)Google Scholar
  11. 11.
    Guy, S.J., Lin, M.C., Manocha, D.: Modelling Collision Avoidance Behavior for Virtual Humans. In: 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, pp. 575–582 (May 2010)Google Scholar
  12. 12.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating Dynamical Features of Escape Panic. Physical Review E cond-mat.stat-mech (September 2000)Google Scholar
  13. 13.
    Helbing, D., Molnar, P.: Social Force Model for Pedestrian Dynamics. Physical Review E cond-mat.stat-mech, 4282–4286 (1995)CrossRefGoogle Scholar
  14. 14.
    Hill, R.W.: Modeling Perceptual Attention in Virtual Humans. In: Computer Generated Forces and Behavioral Representation, Orlando, pp. 1–11 (May 1999)Google Scholar
  15. 15.
    Hochberg, J., McAlister, E.: A quantitative approach, to figural ”goodness”. Journal of Experimental Psychology 46(5), 361–364 (1953)CrossRefGoogle Scholar
  16. 16.
    Itti, L., Koch, C.: Computational modeling of visual attention. Nature 2, 194–203 (2001)Google Scholar
  17. 17.
    Kamphuis, A., Overmars, M.H.: Finding paths for coherent groups using clearance. In: Proceedings of the 2004 ACM SIGGRAPH Symposium on Computer Animation, Utrecht University (2004)Google Scholar
  18. 18.
    Kim, Y., van Velsen, M., Hill Jr., R.W.: Modeling Dynamic Perceptual Attention in Complex Virtual Environments. In: Panayiotopoulos, T., Gratch, J., Aylett, R.S., Ballin, D., Olivier, P., Rist, T. (eds.) IVA 2005. LNCS (LNAI), vol. 3661, pp. 266–277. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Klein, W., Köster, G., Meister, A.: Towards the Calibration of Pedestrian Stream Models. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2009, Part II. LNCS, vol. 6068, pp. 521–528. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Klein, W., Köster, G., Meister, A.: Towards the Calibration of Pedestrian Stream Models. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2009, Part II. LNCS, vol. 6068, pp. 521–528. Springer, Heidelberg (2010), http://dl.acm.org/citation.cfm?id=1893586.1893650 CrossRefGoogle Scholar
  21. 21.
    Klüpfel, H., Schreckenberg, M., Meyer-König, T.: Models for crowd Movement and egress Simulation. Traffic and Granular Flow (2005)Google Scholar
  22. 22.
    Kuligowski, E.D.: The Process of Human Behavior in Fire. Tech. rep., National Institute of Standards and Technological (May 2009)Google Scholar
  23. 23.
    Luo, L., Zhou, S., Cai, W., Low, M.Y.H., Tian, F., Wang, Y., Xiao, X., Chen, D.: Agent-based human behavior modeling for crowd simulation. Computer Animation and Virtual Worlds 19(3-4), 271–281 (2008)CrossRefGoogle Scholar
  24. 24.
    Miller, G.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review (1956)Google Scholar
  25. 25.
    Okazaki, S., Matsushita, S.: A study of simulation model for pedestrian movement with evacuation and queuing. Engineering for Crowd Safety (1993)Google Scholar
  26. 26.
    O’Reagan, J.K., Rensink, R.A., Clark, J.J.: Change-blindness as a result of ’mudsplashes’. Nature 398, 34 (1999)CrossRefGoogle Scholar
  27. 27.
    Ozel, F.: Time pressure and stress as a factor during emergency egress. Safety Science 38, 95–107 (2001)CrossRefGoogle Scholar
  28. 28.
    Pettré, J., Ondřej, J., Olivier, A.H., Cretual, A., Donikian, S.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: SCA 2009: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. ACM Request Permissions (August 2009)Google Scholar
  29. 29.
    Reynolds, C.: Flocks, herds and schools: A distributed behavioral model. Computer Graphics 21(4), 25–34 (1987)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Song, Q., Kasabov, N.: Ecm - a novel on-line, evolving clustering method and its applications. In: Posner, M.I. (ed.) Foundations of Cognitive Science, pp. 631–682. The MIT Press (2001)Google Scholar
  31. 31.
    Still, G.K.: Crowd dynamics. Ph.D. thesis, University of Warwick, University of Warwick, Department of Mathematics (August 2000)Google Scholar
  32. 32.
    Triesch, J., Ballard, D.H., Hayhoe, M.M., Sullivan, B.T.: What you see is what you need. Journal of Vision 3, 1–9 (2003)CrossRefGoogle Scholar
  33. 33.
    Whittle, M.: Gait Analysis. An Introduction, 4th edn. Butterworth Heinemann (December 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vaisagh Viswanathan
    • 1
  • Michael Lees
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

Personalised recommendations