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Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective

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Modeling, Simulation and Visual Analysis of Crowds

Part of the book series: The International Series in Video Computing ((VICO,volume 11))

Abstract

Over the last several years there has been a growing interest in developing computational methodologies for modeling and analyzing movements and behaviors of ‘crowds’ of people. This interest spans several scientific areas that includes Computer Vision, Computer Graphics, and Pedestrian Evacuation Dynamics. Despite the fact that these different scientific fields are trying to model the same physical entity (i.e. crowd of people), research ideas have evolved independently. As a result each discipline has developed techniques and perspectives that are characteristically it’s own. In this chapter we provide a brief overview of major research themes from these different scientific fields, discuss common challenges and point to problem areas that will benefit from common synthesis of perspectives from these fields. In addition we introduce various pieces of work that appear in this monograph as separate chapters.

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References

  1. Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 555–560 (2008)

    Article  Google Scholar 

  2. Ali, S., Shah, M.: A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 17–22 June 2007, pp. 1–6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4270002&isnumber=4269956

  3. Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Proceedings of European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 5303 (2008)

    Google Scholar 

  4. Andrade, E.L., Blunsden, S., Fisher, R.B.: Modelling crowd scenes for event detection. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 1, pp. 175–178 (2006). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1698931&isnumber=35817

  5. Andrade, E.L., Blunsden, S., Fisher, R.B.: Hidden Markov models for optical flow analysis in crowds. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 1, pp. 460–463 (2006).. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1698931&isnumber=35817

  6. Bayazit, O.B., Lien, J.-M., Amato, N.M.: Better group behaviors in complex environments with global roadmaps. In: Standish, R.K., Bedau, M.A., Abbass, H.A. (eds.) International Conference on the Simulation and Synthesis of Living Systems (ICAL 2003) (Alife), pp. 362–370. MIT, Cambridge (2002)

    Google Scholar 

  7. Betke, M., Hirsh, D.E., Bagchi, A., Hristov, N.I., Makris, N.C., Kunz, T.H.: Tracking large variable numbers of objects in clutter. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR ’07), 17–22 June 2007, pp. 1–8 (2007). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4270019&isnumber=4269956

  8. Brostow, G.J., Cipolla, R.: Unsupervised Bayesian detection of independent motion in crowds. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 594–601, 17–22 June 2006. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1640809&isnumber=34373

  9. Cartwright, D., Zander, A.: Group Dynamics: Research and Theory, 3rd edn. Tavistock Publications, London (1968)

    Google Scholar 

  10. Chan, A.B., Vasconcelos, N.: Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 909–26 (2008)

    Article  Google Scholar 

  11. Chattaraj, U., Seyfried, A., Chakroborty, P.: Comparison of pedestrian fundamental diagram across cultures. Adv. Complex Syst. 12(03), 393–405 (2009)

    Article  Google Scholar 

  12. Chenney, S.: Flow tiles. In: Proceedings 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA ’04), pp. 233–242. Eurographics Association, Aire-la-Ville (2004). http://dx.doi.org/10.1145/1028523.1028553

  13. Cheriyadat, A., Radke, R.: Detecting dominant motions in dense crowds. IEEE J. Sel. Top. Signal Process. 2(4), 568–581 (2008)

    Article  Google Scholar 

  14. Dee, H., Hogg, D.: Detecting inexplicable behaviour. In: Proceedings of British Macine Vision Conference, The British Machine Vision Association, pp. 477–486 (2004)

    Google Scholar 

  15. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  16. Dobbyn, S., Hamill, J., O’Conor, K., O’Sullivan, C.: Geopostors: a realtime geometry/impostor crowd rendering system. In: Proceedings of the Symposium on Interactive 3D Graphics and Games, New York, pp. 95–102 (2005)

    Google Scholar 

  17. Gayle, R., Sud, A., Andersen, E., Guy, S., Lin, M., Manocha, D.: Interactive navigation of independent agents using adaptive roadmaps. IEEE Trans. Vis. Comput. Graph. 15(1), 34–48 (2009)

    Article  Google Scholar 

  18. Ge, W., Collins, R., Ruback, B.: Vision-based analysis of small groups in pedestrian crowds. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 1003–1016 (2012)

    Article  Google Scholar 

  19. Gilbert, A., Bowden, R.: Multi person tracking within crowded scenes. In: Elgammal, A., Rosenhahn, B., Klette R. (eds.) IEEE Workshop on Human Motion, pp. 166–179. Springer, Berlin/Heidelberg (2007)

    Google Scholar 

  20. Guy, S.J., Chhugani, J., Kim, C., Satish, N., Lin, M.C., Manocha, D., Dubey, P.: Clearpath: highly parallel collision avoidance for multi-agent simulation. In: Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 177–187 (2009)

    Google Scholar 

  21. Guy, S., Chuggani, J., Curtis, S., Dubey, P., Lin, M., Manocha, D.: Pledestrians: a least-effort approach to crowd simulation. In: Proceedings of Eurographics/ACM SIGGRAPH Symposium on Computer Animation, pp. 119–128 (2010)

    Google Scholar 

  22. Guy, S., Kim, S., Lin, M., Manocha, D.: Simulating heterogeneous crowd behaviors using personality trait theory. In: Proceedings of Eurographics/ACM SIGGRAPH Symposium on Computer Animation, pp. 43–52 (2011)

    Google Scholar 

  23. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Helbing, D., Molnar, P., Farkas, I.J., Bolay, K.: Self-organizing pedestrian movement. Environ. Plan. B Plan. Des. 28(3), 361–383 (2001)

    Article  Google Scholar 

  26. Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1450–1464 (2006)

    Article  Google Scholar 

  27. Hu, M., Ali, S., Shah, M.: Detecting global motion patterns in complex videos. In: Proceedings of International Conference on Pattern Recognition (ICPR 2008), Dec 2008, pp. 1–5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4760950&isnumber=4760915

  28. Hu, M., Ali, S., Shah, M.: Learning motion patterns in crowded scenes using motion flow field. In: Proceedings of International Conference on Pattern Recognition (ICPR 2008), pp. 1–5, 8–11 Dec (2008). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4761183&isnumber=4760915

  29. Hue, C., Le Cadre, J.-P., Perez, P.: Posterior Cramer-Rao bounds for multi-target tracking. IEEE Trans. Aerosp. Electron. Syst. 42(1), 37–49 (2006)

    Google Scholar 

  30. Hughes, R.: A continuum theory for the flow of pedestrians. Transp. Res. B Methodol. 36(6), 507–535 (2002)

    Article  Google Scholar 

  31. Hughes, R.L.: The flow of human crowds. Ann. Rev. Fluid Mech. 35, 169–182 (2003)

    Article  Google Scholar 

  32. Izadinia, H., Saleemi, I., Li, W., Shah, M.: (MP)2T: multiple people multiple parts tracker. In: Computer Vision – ECCV. Lecture Notes in Computer Science, vol. 7577, pp. 100–114 (2012)

    Article  Google Scholar 

  33. Jin, X., Xu, J., Wang, C.C.L., Huang, S., Zhang, J.: Interactive control of large crowd navigation in virtual environment using vector field. IEEE Comput. Graph. and Appl. 28(6):37–46 (2008). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4670099&isnumber=4670088

  34. Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: Proceedings of British Macine Vision Conference, pp. 583–592 (1995)

    Google Scholar 

  35. Kagarlis, M.: Method and apparatus of simulating movement of an autonomous entity through an environment. United States Patent No. US 7,188,056, Sept. 2002

    Google Scholar 

  36. Kallmann, M.: Shortest paths with arbitrary clearance from navigation meshes. In: Proceedings ACM SIGGRAPH Eurographics Symposium on Computer Animation (SCA ’10), pp. 159–168. Eurographics Association, Aire-la-Ville (2010)

    Google Scholar 

  37. Kamphuis, A., Overmars, M.: Finding paths for coherent groups using clearance. In: Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA ’04), pp. 19–28. Eurographics Association, Aire-la-Ville (2004). http://dx.doi.org/10.1145/1028523.1028526

  38. Karamouzas, I., Heil, P., Beek, P., Overmars, M.: A predictive collision avoidance model for pedestrian simulation. In: Proceedings of Motion in Games, pp. 41–52 (2009)

    Google Scholar 

  39. Kavraki, L., Svestka, P., Latombe, J., Overmars, M.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)

    Article  Google Scholar 

  40. Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1805–1819 (2005)

    Article  Google Scholar 

  41. Khan, Z., Balch, T., Dellaert, F.: MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1960–1972 (2006)

    Article  Google Scholar 

  42. Kim, J., Grauman, K.: Observe locally, infer globally, a spacetime MRF for detecting abnormal activities with incremental updates. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR2009), 20–25 June 2009, pp. 2921–2928. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5206569&isnumber=5206488

  43. Kim, S., Guy, S., Manocha, D., Lin, M.C.: Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory. In: Proceedings of Interactive 3D Graphics Symposium (2012)

    Google Scholar 

  44. Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition(CVPR 2009), 20–25 June 2009, pp. 1446–1453. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5206771&isnumber=5206488

  45. Kratz, L., Nishino, K.: Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5540149&isnumber=5539770

  46. Kratz, L., Nishino, K.: Going with the flow: pedestrian efficiency in crowded scenes. In: Proceedings of European Conference on Computer Vision (ECCV 2012). Lecture Notes in Computer Science, vol. 7575, pp. 558–572 (2012)

    Article  Google Scholar 

  47. Kratz, L., Nishino, K.: Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 987–1002 (2012)

    Article  Google Scholar 

  48. Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Boston (1991)

    Book  Google Scholar 

  49. LaValle, S.M.: Planning Algorithms. Cambridge University Press (2006). Also available at http://msl.cs.uiuc.edu/planning/

  50. Le Bon, G.: The Crowd: A Study of the Popular Mind Macmillan, New York (1896). Reprint available from Dover Publications

    Google Scholar 

  51. Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20–25 June 2005, vol. 1. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467359&isnumber=31472

  52. Lerner, A., Chrysanthou, Y., Shamir, A., Cohen-Or, D.: Data driven evaluation of crowds. In: Proceedings of the 2nd International Workshop on Motion in Games. Lecture Notes in Computer Science vol. 5884, pp. 75–83 (2009)

    Article  Google Scholar 

  53. Lewin, K.: In: Cartwright, D. (ed.) Field Theory in Social Science; Selected Theoretical Papers. Harper & Row, New York (1951)

    Google Scholar 

  54. Li, Y., Huang, C., Nevatia, R.: Learning to associate: hybridboosted multi-target tracker for crowded scene. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20–25 June 2009. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5206735&isnumber=5206488

  55. Lin, D., et al.: Modeling and estimating persistent motion with geometric flows. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13–18 June 2010, pp.1–8. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5539848&isnumber=5539770

  56. Lozano-Pérez, T., Wesley, M.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22(10), 560–570 (1979)

    Article  Google Scholar 

  57. Magnenat-Thalmann, N., Seo, H., Cordier, F.: Automatic modeling of virtual humans and body clothing. J. Comput. Sci. Technol. 19(5), 575–584 (2004)

    Article  Google Scholar 

  58. Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 13–18 June 2010, pp. 1975–1981 (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5539872&isnumber=5539770

  59. Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: CVPR (2010)

    Google Scholar 

  60. Maïm, J., Yersin, B., Pettré, J., Thalmann, D.: YaQ: an architecture for real-time navigation and rendering of varied crowds. IEEE Comput. Graph. Appl. 29(4), 44–53 (2009)

    Article  Google Scholar 

  61. McDonnell, R., Larkin, M., Dobbyn, S., Collins, S., O’Sullivan, C.: Clone attack! perception of crowd variety. ACM Trans. Graph. 27(3), 1–8 (2008)

    Article  Google Scholar 

  62. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20–25 June 2009, pp. 935–942. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5206641&isnumber=5206488

  63. Mehran, R., Moore, B.E., Shah, M.: A streakline representation of flow in crowded scenes. In: European Conference on Computer Vision (ECCV 2010). Lecture Notes in Computer Science, vol. 6313, pp. 439–452 (2010)

    Article  Google Scholar 

  64. Moore, B.E., Ali, S., Mehran, R., Shah, M.: Visual crowd surveillance through a hydrodynamics lens. Commun. ACM 54, 64–73 (2011)

    Article  Google Scholar 

  65. Musse, S.R., Thalmann, D.: A hierarchical model for real time simulation of virtual human crowds. IEEE Trans. Vis. Comput. Graph. 7(2), 152–164 (2001)

    Article  Google Scholar 

  66. Narain, R., Golas, A., Curtis, S., Lin, M.C.: Aggregate dynamics for dense crowd simulation. ACM Trans. Graph. 28(5), 1–8 (2009)

    Article  Google Scholar 

  67. Patil, S., van den Berg, J., Curtis, S., Lin, M.C., Manocha, D.: Directing crowd simulations using navigation fields. IEEE Trans. Vis. Comput. Graph. 17(2), 244–254 (2011)

    Google Scholar 

  68. Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA ’07), pp. 99–108. Eurographics Association, Aire-la-Ville (2007)

    Google Scholar 

  69. Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You will never walk alone: modeling social behavior for multi-target tracking. In: Proceedings of IEEE International Conference on Computer Vision and Pattern, Sept. 29-Oct. 2 2009, pp. 261–268. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5459260&isnumber=5459144

  70. Pettré, J., de Heras Ciechomski, P., Maïm, J., Yersin, B., Laumond, J.-P., Thal-mann, D.: Real-time navigating crowds: scalable simulation and rendering. J. Vis. Comput. Animat. 17(3–4), 445–455 (2006)

    Google Scholar 

  71. Pettre, J., Grillon, H., Thalmann, D.: Crowds of moving objects: navigation planning and simulation. In: Proceedings of IEEE International Conference on Robotics and Automation, 10–14 April 2007, pp. 3062–3067. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4209555&isnumber=4209049

  72. Pettre, J., Ondrej, J., Olivier, A., Cretual, A., Donikian, S.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA ’09), pp. 189–198. ACM (2009). http://doi.acm.org/10.1145/1599470.1599495

  73. Raghavendra, R., Del Bue, A., Cristani, M., Murino, V.: Optimizing interaction force for global anomaly detection in crowded scenes. In: Proceedings of IEEE International Conference on Computer Vision (ICCV Workshops), 6–13 Nov 2011, pp. 136–143. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6130235&isnumber=6130192

  74. Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. Proc. ACM SIGGRAPH 21, 25–34 (1987)

    Article  Google Scholar 

  75. Reynolds, C.W.: Steering behaviors for autonomous characters. In: Game Developers Conference (1999)

    Google Scholar 

  76. Rodriguez, M.D., Shah, M.: Detecting and segmenting humans in crowded scenes. In: Proceedings of the 15th International Conference on Multimedia (MULTIMEDIA ’07), pp. 353–356. ACM, New York (2007). http://doi.acm.org/10.1145/1291233.1291310

  77. Rodriguez, M., Ali, S., Kanade, T.: Tracking in unstructured crowded scenes. In: Proceedings of IEEE International Conference on Computer Vision, 29 Sept-2 Oct 2009, pp. 1389–1396. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5459301&isnumber=5459144

  78. Rodriguez, M., Sivic, J., Laptev, I., Audibert, J.: Density-aware person detection and tracking in crowds. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), 6–13 Nov 2011, pp. 2423–2430. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6126526&isnumber=6126217

  79. Saleemi, I., Hartung, L., Shah, M.: Scene understanding by statistical modeling of motion patterns. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13–18 June 2010, pp. 2069–2076. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5539884&isnumber=5539770

  80. Sarmady, S., Haron, F., Hj, A.Z.: Modeling groups of pedestrians in least effort crowd movements using cellular automata. In: Proceedings of 3rd Asia International Conference on Modeling and Simulation (AMS ’09), pp. 520–525. IEEE Computer Society, Washington, DC (2009). http://dx.doi.org/10.1109/AMS.2009.16

  81. Scovanner, P., Tappen, M.: Learning pedestrian dynamics from the real world. In: Proceedings of IEEE International Conference on Computer Vision, 29 Sept-2 Oct 2009, pp. 381–388. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5459224&isnumber=5459144

  82. Seyfried, A., Boltes, M., Kähler, J., Klingsch, W., Portz, A., Rupprecht, T., Schadschneider, A., Steffen, B., Winkens, A.: Enhanced empirical data for the fundamental diagram and the flow through bottlenecks. In: Klingsch, W.W.F., Rogsch, C., Schadschneider, A., Schreckenberg, M. (eds.) Pedestrian and Evacuation Dynamics 2008, pp. 145–156. Springer, Berlin/Heidelberg (2010)

    Chapter  Google Scholar 

  83. Shao, W., Terzopoulos, D.: Autonomous pedestrians. In: SCA ’05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA ’05), pp. 19–28. ACM, New York (2005). http://doi.acm.org/10.1145/1073368.1073371

  84. Simeon, T., Leroy, S., Laumond, J.: Path coordination for multiple mobile robots: a geometric algorithm. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1118–1123 (1999)

    Google Scholar 

  85. Snape, J., van den Berg, J., Guy, S.J., Manocha, D.: Independent navigation of multiple mobile robots with hybrid reciprocal velocity obstacles. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, pp. 5917–5922 (2009)

    Google Scholar 

  86. Snook, G.: Simplified 3D movement and pathfinding using navigation meshes. In: DeLoura, M.A. (ed.) Game Programming Gems, pp. 288–304. Charles River, Hingham (2000). Chapter 3

  87. Still, G.: Crowd dynamics. Ph.D. thesis, University of Warwick (2000)

    Google Scholar 

  88. Still, K.: Crowd dynamics. Ph.D. thesis, University of Warwick (2000)

    Google Scholar 

  89. Sud, A., Andersen, E., Curtis, S., Lin, M., Manocha, D.: Real-time path planning for virtual agents in dynamic environments. In: ACM SIGGRAPH 2008 classes (SIGGRAPH ’08), Article 55, 9pp. ACM, New York (2008)

    Google Scholar 

  90. Sugimura, D., Kitani, K., Okabe, T., Sato, Y., Sugimoto, A.: Using individuality to track individuals: clustering individual trajectories in crowds using local appearance and frequency trait. In: Proceedings of IEEE International Conference on Computer Vision, 29 Sept-2 Oct 2009, pp.1467–1474. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5459286&isnumber=5459144

  91. Treuille, A., Cooper, S., Popovic, Z.: Continuum crowds. ACM Trans. Graph. 25(3), 1160–1168 (2006)

    Article  Google Scholar 

  92. van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: International Symposium of Robotics Research. Robotics Research Springer Tracts in Advanced Robotics, vol. 70, pp. 3–19 (2009)

    Google Scholar 

  93. van den Berg, J., Seawall, J., Lin, M.C., Manocha, D.: Virtualized traffic: reconstructing traffic flows from discrete spatio-temporal data. Proc. IEEE Trans. Vis. Comput. Grap. 17(1), 26–37 (2009). IEEE Computer Society. http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.27

  94. van Toll, W., Cook, A.F., Geraerts, R.: Navigation meshes for realistic multi-layered environments. In: Proceedings of IEEE RSJ International Conference on Intelligent Robots and Systems, 25–30 Sept 2011, pp. 3526–3532. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6094790&isnumber=6094399

  95. Wang, X., Tieu, K., Grimson, E.: Learning semantic scene models by trajectory analysis. In: Proceedings of European Conference on Computer Vision, pp. 110–123 (2006)

    Google Scholar 

  96. Warren, C.W.: Multiple path coordination using artificial potential fields. In: Proceedings of IEEE Conference on Robotics and Automation, 13–18 May 1990, vol. 1, pp. 500–505. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=126028&isnumber=3534

  97. Wu, B., Nevatia, R.: Tracking of multiple, partially occluded humans based on static body part detection. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 951–958 (2006)

    Google Scholar 

  98. Wu, S., et al.: Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13–18 June 2010, pp. 2054–2060. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5539882&isnumber=5539770

  99. Yamaguchi, K., Berg, C., Ortiz, L.E., Berg, T.L.: Who are you with and where are you going? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 20–25 June 2011, pp.1345–1352. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995468&isnumber=5995307

  100. Yersin, B., Maim, J., Ciechomski, P., Schertenleib, S., Thalmann, D.: Steering a virtual crowd based on a semantically augmented navigation graph. In: VCROWDS (2005)

    Google Scholar 

  101. Yu, Q., Terzopoulos, D.: A decision network framework for the behavioral animation of virtual humans. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA ’07), pp. 119–128. Eurographics Association, Aire-la-Ville (2007)

    Google Scholar 

  102. Zhan, B., Monekosso, D., Remagnino, P., Velastin, S., Xu, L.-Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19(5), 345–357 (2008)

    Article  MATH  Google Scholar 

  103. Zhao, T., Nevatia, R., Wu, B.: Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1198–1212 (2008)

    Article  Google Scholar 

  104. Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley Press, Cambridge (1949)

    Google Scholar 

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Acknowledgements

We like to acknowledge Army Research Office, Nippon Telegraph and Telephone, National Science Foundation, and Office of Naval Research for providing support for writing this chapter. We also like to thank Louis Kratz for his contribution to this chapter.

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  1. Center for Vision Technologies, SRI International, 201 Washington Road, Princeton, NJ, USA

    Saad Ali

  2. Department of Computer Science, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA

    Ko Nishino

  3. University of North Carolina, Chapel Hill, NC, USA

    Dinesh Manocha

  4. University of Central Florida, Orlando, FL, USA

    Mubarak Shah

Authors
  1. Saad Ali
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  2. Ko Nishino
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  3. Dinesh Manocha
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  4. Mubarak Shah
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Correspondence to Saad Ali .

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Editors and Affiliations

  1. Center for Vision Technologies, SRI International, Princeton, New Jersey, USA

    Saad Ali

  2. Department of Computer Science, Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  3. Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA

    Dinesh Manocha

  4. Center for Research in Computer Vision, University of Central Florida, Orlando, Florida, USA

    Mubarak Shah

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© 2013 Springer Science+Business Media New York

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Ali, S., Nishino, K., Manocha, D., Shah, M. (2013). Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective. In: Ali, S., Nishino, K., Manocha, D., Shah, M. (eds) Modeling, Simulation and Visual Analysis of Crowds. The International Series in Video Computing, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8483-7_1

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  • DOI: https://doi.org/10.1007/978-1-4614-8483-7_1

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