The Visual Computer

, Volume 31, Issue 6–8, pp 843–852 | Cite as

Real-time collision-free linear trajectory generation on GPU for crowd simulations

Original Article

Abstract

Crowd simulations are mostly employed to compose a background in the current scene. For such ambient crowds, it might be unnecessary to perform complex steering calculations. In this study, a steering-free crowd simulation which eliminates the computational cost arising from expensive steering maneuvers is presented. For this purpose, agents are assigned a linear trajectory that is guaranteed to be collision free before entering the simulation. These trajectories are calculated using readily available rendering pipeline of the GPU. To that end, existing agents’ bounding discs are rendered in a spatio-temporal manner as each one forms a straight 3D tube and a projection from a selected initial position is captured. Using the blank areas (holes) in this image, it is possible to determine a suitable constant velocity (a goal position and a speed). In experiments, we not only assess three different methods to choose one of the candidate solutions, but also compare our approach with an existing work. Test results reveal that our technique gives better results in both populating an empty environment with agents quicker and reaching a higher maximum number of agents than the existing method.

Keywords

Crowd simulation Crowd navigation Ambient crowd 

Supplementary material

Supplementary material 1 (wmv 8334 KB)

References

  1. 1.
    Ahn, J., Oh, S., Wohn, K.: Optimized motion simplification for crowd animation. Comput. Animat. Virtual Worlds 17(3–4), 155–165 (2006)CrossRefGoogle Scholar
  2. 2.
    Atomic counter (2015). https://www.opengl.org/wiki/Atomic_Counter. Accessed 31 Jan 2015
  3. 3.
    Barut, O., Haciomeroglu, M., Ozcan, C.Y.: Illusive crowd. In: CASA 2014 (2014)Google Scholar
  4. 4.
    van den Berg, J., Guy, S., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: Symposium on Robotics Research (2009)Google Scholar
  5. 5.
    Chang, F., Chen, C., Lu, C.: A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Image Underst. 93(2), 206–220 (2004)CrossRefGoogle Scholar
  6. 6.
    Cormen, T.H., Stein, C., Rivest, R.L., Leiserson, C.E.: Introduction to Algorithms, 2nd edn. McGraw-Hill Higher Education, MIT press Cambridge London. ISBN: 0262032937 (2001)Google Scholar
  7. 7.
    Dillencourt, M.B., Samet, H., Tamminen, M.: A general approach to connected-component labeling for arbitrary image representations. J. ACM 39(2), 253–280 (1992)MATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Dobbyn, S., Hamill, J., O’Conor, K., O’Sullivan, C.: Geopostors: a real-time geometry / impostor crowd rendering system. In: Proceedings of the 2005 Symposium on Interactive 3D Graphics and Games. I3D ’05, pp. 95–102. ACM, New York (2005)Google Scholar
  9. 9.
    Haciomeroglu, M., Barut, O., Ozcan, C.Y., Sever, H.: A gpu-assisted hybrid model for real-time crowd simulations. Comput. Gr. 37(7), 862–872 (2013)CrossRefGoogle Scholar
  10. 10.
    Haralick, R.: Some neighborhood operators. In: Onoe, M., Preston Kendall, J., Rosenfeld, A. (eds.) Real-Time Parallel Computing, pp. 11–35. Springer, New York (1981)CrossRefGoogle Scholar
  11. 11.
    Hawick, K.A., Leist, A., Playne, D.P.: Parallel graph component labelling with gpus and cuda. Parallel Comput. 36(12), 655–678 (2010)MATHCrossRefGoogle Scholar
  12. 12.
    Kulpa, R., Olivierxs, A., Ondřej, J., Pettré, J.: Imperceptible relaxation of collision avoidance constraints in virtual crowds. ACM Trans. Gr. 30(6), 138:1–138:10 (2011)CrossRefGoogle Scholar
  13. 13.
    Kuwahara, M., Sato, T., Yotsumoto, Y.: Wriggling motion trajectory illusion. J. Vis. 12(12), 1–14 (2012)CrossRefGoogle Scholar
  14. 14.
    Luebke, D., Watson, B., Cohen, J.D., Reddy, M., Varshney, A.: Level of Detail for 3D Graphics. Elsevier Science Inc., New York (2002)Google Scholar
  15. 15.
    Lumia, R., Shapiro, L., Zuniga, O.: A new connected components algorithm for virtual memory computers. Comput. Vis. Gr. Image Process. 22(2), 287–300 (1983)CrossRefGoogle Scholar
  16. 16.
    Narain, R., Golas, A., Curtis, S., Lin, M.: Aggregate dynamics for dense crowd simulation. ACM Trans. Gr. 28, 122:1–122:8 (2009)CrossRefGoogle Scholar
  17. 17.
    Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: SCA’07, pp. 99–108, Switzerland (2007)Google Scholar
  18. 18.
    Reynolds, C.W.: Steering behaviors for autonomous characters. In: Game Developers Conference ’09, pp. 763–782 (1999)Google Scholar
  19. 19.
    Rosenfeld, A.: Connectivity in digital pictures. J. ACM 17(1), 146–160 (1970)MATHMathSciNetCrossRefGoogle Scholar
  20. 20.
    Soh, Y., Ashraf, H., Hae, Y., Kim, I.: A hybrid approach to parallel connected component labeling using cuda. Int. J. Signal Process. Syst. 1(2), 130–135 (2013)CrossRefGoogle Scholar
  21. 21.
    Stava, O., Benes, B.: Connected component labeling in cuda. In: GPU Computing Gems Emerald Edition, p. 569 (2010)Google Scholar
  22. 22.
    di Stefano, L., Bulgarelli, A.: A simple and efficient connected components labeling algorithm. In: Proceedings of the 10th International Conference on Image Analysis and Processing, ICIAP ’99, pp. 322-. IEEE Computer Society, Washington, DC (1999)Google Scholar
  23. 23.
    Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Comput. Vis. Image Underst. 89(1), 1–23 (2003)MATHCrossRefGoogle Scholar
  24. 24.
    Thalmann, N., Thalmann, D.: Virtual humans: thirty years of research, what next? Vis. Comput. 21(12), 997–1015 (2005)CrossRefGoogle Scholar
  25. 25.
    Treuille, A., Cooper, S., Popovic, Z.: Continuum crowds. In: SIGGRAPH, pp. 1160–1168 (2006)Google Scholar
  26. 26.
    Wu, K., Otoo, E., Suzuki, K.: Optimizing two-pass connected-component labeling algorithms. Pattern Anal. Appl. 12(2), 117–135 (2009)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Yersin, B., Maïm, J., Pettré, J., Thalmann, D.: Crowd patches: populating large-scale virtual environments for real-time applications. In: I3D’09, pp. 207–214 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer EngineeringHacettepe UniversityAnkaraTurkey
  2. 2.Department of Computer EngineeringGazi UniversityAnkaraTurkey

Personalised recommendations