Machine Vision and Applications

, Volume 19, Issue 5–6, pp 345–357 | Cite as

Crowd analysis: a survey

  • Beibei Zhan
  • Dorothy N. Monekosso
  • Paolo Remagnino
  • Sergio A. Velastin
  • Li-Qun Xu


In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.


Crowd studies Crowd dynamics Socio-dynamics Crowd simulations Computer vision 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    2004-2005, 2005-2006, CIFE, Seed, Project, Stanford, University:
  2. 2.
    Adang, O.M., Stott, C.: A European study of the interaction between police and crowds of foreign nationals considered to pose a risk to public order.
  3. 3.
  4. 4.
    AEA, Techology: a technical summary of the aea egress code. Technical Report 1 (2002)Google Scholar
  5. 5.
    Andrade, E., Fisher, R.: Simulation of crowd problems for computer vision. In: First International Workshop on Crowd Simulation, vol. 3, pp. 71–80 (2005)Google Scholar
  6. 6.
    Andrade, E., Fisher, R.: Hidden Markov models for optical flow analysis in crowds. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), vol. 01, pp. 460–463. IEEE Computer Society Washington, (2006)Google Scholar
  7. 7.
    Andrade, E., Fisher, R.: Modelling crowd scenes for event detection. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), vol. 01, pp. 175–178. IEEE Computer Society Washington, DC (2006)Google Scholar
  8. 8.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Performance analysis of event detection models in crowded scenes. In: Proceedings of Workshop on Towards Robust Visual Surveillance Techniques and Systems at Visual Information Engineering 2006, pp. 427–432. Bangalore, India (2006)Google Scholar
  9. 9.
    Antonini, G., Bierlaire, M., Weber, M.: Simulation of pedestrian behaviour using a discrete choice model calibrated on actual motion data. In: 4th STRC Swiss Transport Research Conference. Ascona (2004)Google Scholar
  10. 10.
    Antonini, G., Venegas, S., Thiran J.P., Bierlaire, M.: A discrete choice pedestrian behaviour model in visual tracking systems. In: Advanced Concepts for Intelligent Vision Systems, pp. 273–280. Brussels, Belgium (2004)Google Scholar
  11. 11.
    Banarjee, S., Grosan, C., Abarha, A.: Emotional ant based modeling of crowd dynamics. In: Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC’05), pp. 279–286 (2005)Google Scholar
  12. 12.
  13. 13.
    Blackman, S.: Multiple hypothesis tracking for multiple target tracking. Aerospace Electron. Syst. Mag. IEEE 19(1), 5–18 (2004)CrossRefGoogle Scholar
  14. 14.
    Boghossian, B., Velastin, S.: Motion-based machine vision techniques for the management of large crowds. In: The 6th IEEE International Conference on Electronics, Circuits and Systems, vol. 2 (1999)Google Scholar
  15. 15.
    Brenner, M., Wijermans, N., Nussle, T., de Boer, B.: Simulating and controlling civilian crowds in robocup rescue. In: Proceedings of RoboCup 2005: Robot Soccer World Cup IX, Osaka (2005)Google Scholar
  16. 16.
    Broggi, A., Bertozzi, M., Fascioli, A., Sechi, M.: Shape-based pedestrian detection. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2000. Dearbon (MI), USA (2000)Google Scholar
  17. 17.
    Brostow, G., Cipolla, R.: Unsupervised Bayesian detection of independent motion in crowds. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 594–601. IEEE Computer Society Washington, DC, USA (2006)Google Scholar
  18. 18.
    Cai, Y., de Freitas, N., Little, J.J.: Robust visual tracking for multiple targets. In: European Conference on Computer Vision, LNCS, vol. 3954, pp. 107–118. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Chan, M.T., Hoogs, A., Bhotika, R., Perera, A., Schmiederer, J., Doretto, G.: Joint recognition of complex events and track matching. In: CVPR ’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1615–1622. IEEE Computer Society, Washington, DC, USA (2006). doi: 10.1109/CVPR.2006.160
  20. 20.
    Chang, T., Gong, S., Ong, E.: Tracking multiple people under occlusion using multiple cameras. In: British Machine Vision Conference, pp. 566–575 (2000)Google Scholar
  21. 21.
    Chu, J., Li, J., Xu, M., Zhao, L.: Simulating escape panic based on the mechanism of asymmetric information distribution. In: Complex Systems Summer School Final Project Papers. Santa Fe Institute, Santa Fe (2005).Google Scholar
  22. 22.
  23. 23.
  24. 24.
    Cupillard, F., Bremond, F., Thonnat, M.: Behaviour recognition for individuals, groups of people and crowd. IEE Seminar Digests 7 (2003)Google Scholar
  25. 25.
    Cupillard, F., Bremond, F., Thonnat, M., INRIA, F.: Group behavior recognition with multiple cameras. Applications of Computer Vision, 2002 (WACV 2002). In: Proceedings of Sixth IEEE Workshop, pp. 177–183 (2002)Google Scholar
  26. 26.
    Davies, A., Yin, J., Velastin, S.: Crowd monitoring using image processing. Electron. Commun. Eng. J. 7(1), 37–47 (1995)CrossRefGoogle Scholar
  27. 27.
    Dong, L., Parameswaran, V., Ramesh, V., Zoghlami, I.: Fast Crowd Segmentation Using Shape Indexing. Rio de Janeiro, Brazil (2007)Google Scholar
  28. 28.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering (2000)Google Scholar
  29. 29.
    Elgammal, A., Davis, L.: Probabilistic framework for segmenting people under occlusion. In: Eighth IEEE International Conference on Computer Vision, 2001. Proceedings of ICCV 2001, vol. 2, pp. 145–152 (2001)Google Scholar
  30. 30.
    FHWA.: Traffic analysis tools primer,traffic analysis toolbox (1) (2004).
  31. 31.
    Gabriel, P., Verly, J., Piater, J., Genon, A.: The state of the art in multiple object tracking under occlusion in video sequences. Advanced Concepts for Intell. Vis. Syst., pp. 166–173 (2003)Google Scholar
  32. 32.
    Han, M., Xu, W., Tao, H., Gong, Y.: An algorithm for multiple object trajectory tracking. Computer Vision and Pattern Recognition, 2004. In: CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference, vol. 1 (2004)Google Scholar
  33. 33.
    Heisele B., Woehler C. (1998) Motion-based recognition of pedestrians. In: Proceedings of Fourteenth International Conference on Pattern Recognition, 1998, vol. 2, pp. 1325–1330 (1998)Google Scholar
  34. 34.
    Helbing, D.: Models for pedestrian behavior (1992).
  35. 35.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Lett. Nat. 407, 487–490 (2000)CrossRefGoogle Scholar
  36. 36.
    Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995)CrossRefGoogle Scholar
  37. 37.
    Helbing, D., Molnar, P.: Self-organization phenomena in pedestrian crowds (1997).
  38. 38.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst., Man Cybernet. C Appl. Rev. 34(3), 334–352 (2004)CrossRefGoogle Scholar
  39. 39.
    Huang, C., Ai, H., Li, Y., Lao, S.: Vector boosting for rotation invariant multi-view face detection. In: Tenth IEEE Inter- national Conference on Computer Vision, vol. 1, pp. 446–453 (2005)Google Scholar
  40. 40.
    Huang X., Li L., Sim T. (2004) Stereo-based human head detection from crowd scenes. In: International Conference on Image Processing, 2004. ICIP’04, Vol. 2, pp. 1353–1356Google Scholar
  41. 41.
    Hughes, R.: A continuum theory for the flow of pedestrians. Trans. Res. B Methodol. 36(6), 507–535 (2002)CrossRefGoogle Scholar
  42. 42.
  43. 43.
    Isard, M., Blake, A.: A mixed-state CONDENSATION tracker with automatic model-switching. In: IEEE International Conference on Computer Vision, pp. 107–112 (1998).
  44. 44.
  45. 45.
    Jones, M., Viola, P.: Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96 (2003)Google Scholar
  46. 46.
    Kang, H., Kim, D., Bang, S.: Real-time multiple people tracking using competitive condensation. Proc. Int. Conf. Pattern Recogn. 1, 413–416 (2002)Google Scholar
  47. 47.
    Karlsson, R., Gustafsson, F.: Monte Carlo data association for multiple target tracking. Target Tracking: Algorithms and Applications (Ref. No. 2001/174), IEE, vol. 1 (2001)Google Scholar
  48. 48.
    Khan, S.M., Shah, M.: A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: 9th European Conference on Computer Vision, LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006)Google Scholar
  49. 49.
    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)CrossRefGoogle Scholar
  50. 50.
    Kim, K., Davis, L.S.: Multi-camera tracking and segmentation of occluded people on ground plane using search-guided particle filtering. In: European Conference on Computer Vision, LNCS, vol. 3953, pp. 98–109. Springer, Heidelberg (2006)Google Scholar
  51. 51.
    Kirchner, A., Schadschneider, A.: Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Phys. A Stat. Mech. Appl. 312(1–2), 260–276 (2002)zbMATHCrossRefGoogle Scholar
  52. 52.
    Kirkland, J., Maciejewski, A.: A simulation of attempts to influence crowd dynamics. IEEE Int. Conf. Syst. Man Cybernet. 4328–4333 (2003)Google Scholar
  53. 53.
    Koller-Meier, E., Ade, F.: Tracking multiple objects using the Condensation algorithm. Robot. Auton. Syst. 34(2-3), 93–105 (2001)zbMATHCrossRefGoogle Scholar
  54. 54.
    Kong, D., Gray, D., Tao, H.: Counting Pedestrians in crowds using viewpoint invariant training. In: British Machine Vision Conference (2005)Google Scholar
  55. 55.
    Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), vol. 03, pp. 1187–1190 (2006)Google Scholar
  56. 56.
    Kretz, T., Schreckenberg, M.: F.a.s.t.—floor field—and agent-based simulation tool (2006)Google Scholar
  57. 57.
  58. 58.
    Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. CVPR 2005. 1 (2005)Google Scholar
  59. 59.
    Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.: Statistical learning of multi-view face detection. In: Proceedings of the 7th European Conference on Computer Vision-Part IV, pp. 67–81. Springer, London (2002)Google Scholar
  60. 60.
    Lin, S., Chen, J., Chao, H.: Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans. Syst. Man Cybernet. A 31(6), 645–654 (2001)CrossRefGoogle Scholar
  61. 61.
    Ma, R., Li, L., Huang, W., Tian, Q.: On pixel count based crowd density estimation for visual surveillance. IEEE Conf. Cybernet. Intell. Syst. 1 (2004)Google Scholar
  62. 62.
    Marana, A., da Costa, L., Lotufo, R., Velastin, S.: On the efficacy of texture analysis for crowd monitoring. In: Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision, vol. 00, p. 354 (1998)Google Scholar
  63. 63.
    Marana, A., Da Fontoura Costa, L., Lotufo, R., Velastin, S.: Estimating crowd density with Minkowski fractal dimension. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999. ICASSP’99. vol. 6, 3521–3524 (1999)Google Scholar
  64. 64.
    Marana, A., Velastin, S., Costa, L., Lotufo, R.: Estimation of crowd density using image processing. In: IEE Colloquium on Image Processing for Security Applications (Digest No: 1997/074), p. 11 (1997)Google Scholar
  65. 65.
    Marana, A., Velastin, S., Costa, L., Lotufo, R.: Automatic estimation of crowd density using texture. Safety Sci. 28(3), 165–175 (1998)CrossRefGoogle Scholar
  66. 66.
    Marques, J., Jorge, P., Abrantes, A., Lemos, J.: Tracking groups of Pedestrians in video sequences. IEEE 2003 Conf. Comput. Vis. Pattern Recogn. Workshop 9, 101 (2003)CrossRefGoogle Scholar
  67. 67.
    Mathes, T., Piater, J.: Robust non-rigid object tracking using point distribution models. Bri. Mach. Vis. Conf. 2 (2005)Google Scholar
  68. 68.
    Maurin, B., Masoud, O., Papanikolopoulos, N.: Monitoring crowded traffic scenes. In: Proceedings of The IEEE 5th International Conference on Intelligent Transportation Systems, 2002. pp. 19–24 (2002)Google Scholar
  69. 69.
    McKenna, S., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking groups of people. Comput. Vis. Image Understanding 80(1), 42–56 (2000)zbMATHCrossRefGoogle Scholar
  70. 70.
    Mittal, A., Davis, L.: M 2 tracker: a multi-view approach to segmenting and tracking people in a cluttered scene. Int. J. Comput. Vis. 51(3), 189–203 (2003)CrossRefGoogle Scholar
  71. 71.
    Musse, S., Thalmann, D.: A model of human crowd behavior: group inter-relationship and collision detection analysis. Proc. Workshop Comput. Anim. Simul. Eurograph. 97, 39–51 (1997)Google Scholar
  72. 72.
    Okuma, K., Taleghani, A., de Freitas, N., Little, J., Lowe, D.: A boosted particle filter: Multitarget detection and tracking. Eur. Conf. Comput. Vis. 1, 28–39 (2004)Google Scholar
  73. 73.
    Pan, X., Han, C., Dauber, K., Law, K.: Human and social behavior in computational modeling and analysis of egress. Automation in Construction 15(4), 448–461 (2006)CrossRefGoogle Scholar
  74. 74.
    Polus, A., Schofer, J., Ushpiz, A.: Pedestrian Flow and Level of Service. J. Transportation Eng. 109(1), 46–56 (1983)Google Scholar
  75. 75.
  76. 76.
    Rahmalan, H., Nixon, M., Carter, J.: On crowd density estimation for surveillance. The Institution of Engineering and Technology Conference on Crime and Security, pp. 540C–545C (2006)Google Scholar
  77. 77.
    Rasmussen, C., Hager, G.: Joint probabilistic techniques for tracking multi-part objects. Computer Vision and Pattern Recognition, 1998. In: Proceedings of 1998 IEEE Computer Society Conference, pp. 16–21 (1998)Google Scholar
  78. 78.
    Reid, D.: An algorithm for tracking multiple targets. Automat. Contr. IEEE Trans. 24(6), 843–854 (1979)CrossRefGoogle Scholar
  79. 79.
    Reisman, P., Mano, O., Avidan, S., Shashua, A., Ltd, M., Jerusalem, I.: Crowd detection in video sequences. Intell. Vehicles Symp. IEEE, 66–71 (2004)Google Scholar
  80. 80.
    R.R., C., Hughes, R.: Mathematical modelling of a mediaeval battle: the battle of agincourt. Math. Compute. Simul. 64(2), 259–269 (2004)Google Scholar
  81. 81.
    Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: single-frame classification and system level performance. Intelligent Vehicles Symposium, 2004 IEEE pp. 1–6 (2004)Google Scholar
  82. 82.
    Sidenbladh, H., Wirkander, S.: Tracking random sets of vehicles in terrain. In: Proceedings of 2003 IEEE Workshop on Multi-Object Tracking vol. 9, 98 (2003)Google Scholar
  83. 83.
    Siebel, N., Maybank, S.: Fusion of multiple tracking algorithms for robust people tracking. In: European Conference on Computer Vision, pp. 373–387 (2002)Google Scholar
  84. 84.
    Smith, K., Gatica-Perez, D., Odobez, J.: Using particles to track varying numbers of interacting people. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1–01, pp. 962–969 (2005)Google Scholar
  85. 85.
    Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Mach. Vis. Appl. 14(1), 50–58 (2003)CrossRefGoogle Scholar
  86. 86.
    Sullivan, J., Carlsson, S.: Tracking and labelling of interacting multiple targets. In: European Conference on Computer Vision, LNCS, vol. 3953, pp. 619–632. Springer, Heidelberg (2006)Google Scholar
  87. 87.
    Swets, D., Punch, B.: Genetic algorithms for object localization in a complex scene. In: IEEE International Conference on Image Processing, pp. 595–598 (1995)Google Scholar
  88. 88.
  89. 89.
    Velastin, S., Yin, J., Davies, A., Vicencio-Silva, M., Allsop, R., Penn, A.: Automated measurement of crowd density and motion using imageprocessing. Road traffic monitoring and control, 1994. In: Seventh International Conference, pp. 127–132 (1994)Google Scholar
  90. 90.
    Venegas, S., Knebel, S., Thiran, J.: Multi-object tracking using particle filter algorithm on the top-view plan. Technical report,LTS-REPORT-2004-003, EPFL (2004).
  91. 91.
    Vu, V., Bremond, F., Thonnat, M.: Human behaviour visualisation and simulation for automatic video understanding. In: Proceedings of the 10th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG–2002), Plzen–Bory, Czech Republic, pp. 485–492 (2002)Google Scholar
  92. 92.
    Wu, B., Nevatia, R.: Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors. In: Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 1, 90–97 (2005)Google Scholar
  93. 93.
    Wu, B., Nevatia, R.: Tracking of multiple, partially occluded humans based on static body part detection. In: CVPR ’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 951–958 (2006)Google Scholar
  94. 94.
    Yang, D., Gonzalez-Banos, H., Guibas, L.: Counting people in crowds with a real-time network of simple image sensors. In: Proceedings of Ninth IEEE International Conference on Computer Vision, 2003, pp. 122–129 (2003)Google Scholar
  95. 95.
    Yin, J., Velastin, S., Davies, A.: Image Processing Techniques for Crowd Density Estimation Using a Reference Image. Proc. 2nd Asia-Pacific Conf. Comput. Vis. 3, 6–10 (1995)Google Scholar
  96. 96.
    Zhan, B., Remagnino, P., Velastin, S.: Analysing Crowd Intelligence. Second AIxIA Workshop on Ambient Intelligence (2005)Google Scholar
  97. 97.
    Zhan, B., Remagnino, P., Velastin, S.: Mining paths of complex crowd scenes. Lecture Notes in Computer Science pp. 126–133 (2005). ISBN/ISSN 3-540-30750-8Google Scholar
  98. 98.
    Zhan, B., Remagnino, P., Velastin, S.: Visual analysis of crowded pedestrain scenes. XLIII Congresso Annuale AICA, pp. 549–555 (2005)Google Scholar
  99. 99.
    Zhao, T., Nevatia, R.: Tracking multiple humans in complex situations. Pattern Anal. Mach. Intell. IEEE Trans. 26(9), 1208–1221 (2004)CrossRefGoogle Scholar
  100. 100.
    Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol. 2, II–406–II–413 (2004)Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Beibei Zhan
    • 1
  • Dorothy N. Monekosso
    • 1
  • Paolo Remagnino
    • 1
  • Sergio A. Velastin
    • 1
  • Li-Qun Xu
    • 2
  1. 1.Digital Imaging Research CentreKingston UniversityKingston upon ThamesUK
  2. 2.Research and VenturingBT Group PLCLondonUK

Personalised recommendations