Simulated Virtual Crowds Coupled with Camera-Tracked Humans | SpringerLink
Skip to main content

Simulated Virtual Crowds Coupled with Camera-Tracked Humans

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics - Theory and Applications (VISIGRAPP 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 550))

  • 633 Accesses

Abstract

Our objective with this paper is to show how we can couple a group of real people and a simulated crowd of virtual humans. We attach group behaviors to the simulated humans to get a plausible reaction to real people. We use a two stage system: in the first stage, a group of people are segmented from a live video, then a human detector algorithm extracts the positions of the people in the video, which are finally used to feed the second stage, the simulation system. The positions obtained by this process allow the second module to render the real humans as avatars in the scene, while the behavior of additional virtual humans is determined by using a simulation based on a social forces model. Developing the method required three specific contributions: a GPU implementation of the codebook algorithm that includes an auxiliary codebook to improve the background subtraction against illumination changes; the use of semantic local binary patterns as a human descriptor; the parallelization of a social forces model, in which we solve a case of agents merging with each other. The experimental results show how a large virtual crowd reacts to over a dozen humans in a real environment.

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

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahonen, T.: Face description with local binary patterns: application to face recognition. Pattern Anal. Mach. Intell. 28(12), 41–2037 (2006). http://www.ncbi.nlm.nih.gov/pubmed/17108377ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1717463

    Article  Google Scholar 

  2. Banerjee, P., Sengupta, S.: Human motion detection and tracking for video surveillance. In: Proceedings of the National Conference of Tracking and Video Surveillance Activity Analysis, pp. 88–92 (2008)

    Google Scholar 

  3. Van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. Robot. Res. 70, 3–19 (2011). http://www.springerlink.com/index/15814853H6002Q67.pdf

    Article  Google Scholar 

  4. Van den Berg, J., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE International Conference on Robotics and Automation, pp. 1928–1935. IEEE, May 2008. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4543489

  5. Bhuvaneswari, K., Rauf, H.A.: Edgelet based human detection and tracking by combined segmentation and soft decision. In: Control, Automation, Communication and Energy Conservation, 4–9 June 2009. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5204487

  6. Bleiweiss, A.: Multi agent navigation on GPU. White paper, GDC (2009). http://www.cs.uu.nl/docs/vakken/mcrs/papers/28.pdf

  7. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1467360

  8. De Gyves, O., Toledo, L., Rudomín, I.: Comportamientos en simulación de multitudes : revisión del estado del arte. Res. Comput. Sci. 62, 319–334 (2013). Special Issue: Avances en Inteligencia Artificial

    Google Scholar 

  9. Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 90–487 (2000). http://www.ncbi.nlm.nih.gov/pubmed/11028994

    Article  Google Scholar 

  10. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995). http://link.aps.org/doi/10.1103/PhysRevE.51.4282

    Article  Google Scholar 

  11. Huang, T.: Discriminative local binary patterns for human detection in personal album. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8. IEEE, June 2008. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4587800

  12. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foregroundbackground segmentation using codebook model. Real-Time Imaging 11(3), 172–185 (2005)

    Article  Google Scholar 

  13. Lengvenis, P., Simutis, R., Vaitkus, V., Maskeliunas, R.: Application of computer vision systems for passenger counting in public transport. Electron. Electr. Eng. 19(3), 69–72 (2013). http://www.eejournal.ktu.lt/index.php/elt/article/view/1232

    Google Scholar 

  14. Li, M., Zhang, Z., Huang, K., Tan, T.: Rapid and robust human detection and tracking based on omega-shape features. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 2545–2548. IEEE, November 2009. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5414008

  15. Li, T.Y., Wen Lin, J., Liu, Y.L., Ming Hsu, C.: Interactively directing virtual crowds in a virtual environment. Conf. Artif. Real Telexistence vol. 10 (2002). http://dspace2.lib.nccu.edu.tw/bitstream/140.119/15022/1/59.pdf

  16. Millan, E., Hernandez, B., Rudomin, I.: Large crowds of autonomous animated characters using fragment shaders and level of detail. In: Engel, W. (ed.) ShaderX5: Advanced Rendering Techniques, chap. Beyond Pix, pp. 501–510. Charles River Media (2006). http://www.shaderx5.com/TOC.html

  17. Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PloS one 5(4), e10047 (2010)

    Article  Google Scholar 

  18. Mukherjee, S., Das, K.: Omega model for human detection and counting for application in smart surveillance system. Int. J. Adv. Comput. Sci. Appl. 4(2), 167–172 (2013). arXiv preprint arXiv:1303.0633

    Google Scholar 

  19. Ojala, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1017623

    Article  Google Scholar 

  20. Ozturk, O., Yamasaki, T., Aizawa, K.: Tracking of humans and estimation of body/head orientation from top-view single camera for visual focus of attention analysis. In: Computer Vision Workshops (ICCV Workshops), pp. 1020–1027 September 2009. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5457590ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5457590

  21. Pelechano, N., Stocker, C.: Being a part of the crowd: towards validating VR crowds using presence. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 12–16 (2008). http://dl.acm.org/citation.cfm?id=1402407

  22. Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 21(4), 25–34 (1987). http://portal.acm.org/citation.cfm?doid=37402.37406

    Article  Google Scholar 

  23. Rivalcoba, I.J., Rudomin, I.: Segmentación de peatones a partir de vistas aéreas. Res. Comput. Sci. 62, 129–230 (2013)

    Google Scholar 

  24. Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on Riemannian manifolds. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 June 2007. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4270222

  25. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-511–I-518 (2001). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=990517

  26. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. Conf. Comput. Vision 63(2), 153–161 (2003). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1238422

    Article  Google Scholar 

  27. Wang, Y., Dubey, R., Magnenat-Thalmann, N., Thalmann, D.: An immersive multi-agent system for interactive applications. Vis. Comput. 29(5), 323–332 (2012). http://link.springer.com/10.1007/s00371-012-0735-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ivan Rivalcoba .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Rivalcoba, I., Gyves, O.D., Rudomin, I., Pelechano, N. (2015). Simulated Virtual Crowds Coupled with Camera-Tracked Humans. In: Battiato, S., Coquillart, S., Pettré, J., Laramee, R., Kerren, A., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics - Theory and Applications. VISIGRAPP 2014. Communications in Computer and Information Science, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-319-25117-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25117-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25116-5

  • Online ISBN: 978-3-319-25117-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics