Crowd Tracking with Dynamic Evolution of Group Structures

  • Feng Zhu
  • Xiaogang Wang
  • Nenghai Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)


Crowd tracking generates trajectories of a set of particles for further analysis of crowd motion patterns. In this paper, we try to answer the following questions: what are the particles appropriate for crowd tracking and how to track them robustly through crowd. Different than existing approaches of computing optical flows, tracking keypoints or pedestrians, we propose to discover distinctive and stable mid-level patches and track them jointly with dynamic evolution of group structures. This is achieved through the integration of low-level keypoint tracking, mid-level patch tracking, and high-level group evolution. Keypoint tracking guides the generation of patches with stable internal motions, and also organizes patches into hierarchical groups with collective motions. Patches are tracked together through occlusions with spatial constraints imposed by hierarchical tree structures within groups. Coherent groups are dynamically updated through merge and split events guided by keypoint tracking. The dynamically structured patches not only substantially improve the tracking for themselves, but also can assist the tracking of any other target in the crowd. The effectiveness of the proposed approach is shown through experiments and comparison with state-of-the-art trackers.


Spatial Constraint Coherent Motion Scene Structure Social Force Model Crowd Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ali, I., Dailey, M.N.: Multiple human tracking in high-density crowds. Image and Vision Computing 30(12), 966–977 (2012)Google Scholar
  2. 2.
    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 1–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. PAMI 33(8), 1619–1632 (2011)CrossRefGoogle Scholar
  4. 4.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP Journal on Image and Video Processing 2008 (2008)Google Scholar
  5. 5.
    Blei, D.M., Lafferty, J.D.: A correlated topic model of science. Annals of Applied Statistics 1(1), 17–35 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Brostow, G.J., Cipolla, R.: Unsupervised Bayesian detection of independent motion in crowds. In: CVPR (2006)Google Scholar
  7. 7.
    Dinh, T.B., Vo, N., Medioni, G.: Context tracker: Exploring supporters and distracters in unconstrained environments. In: CVPR (2011)Google Scholar
  8. 8.
    Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC (2006)Google Scholar
  9. 9.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Physics Review 51(5), 4282–4286 (1995)CrossRefGoogle Scholar
  10. 10.
    Idrees, H., Warner, N., Shah, M.: Tracking in dense crowds using prominence and neighborhood motion concurrence. Image and Vision Computing 32(1), 14–26 (2014)CrossRefGoogle Scholar
  11. 11.
    Jing, S., Loy, C.C., Wang, X.: Scene-independent group profiling in crowd. In: CVPR (2014)Google Scholar
  12. 12.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. PAMI 34(7), 1409–1422 (2012)CrossRefGoogle Scholar
  13. 13.
    Kratz, L., Nishino, K.: Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes. PAMI 34(5), 987–1002 (2012)CrossRefGoogle Scholar
  14. 14.
    Le Bon, G.: The crowd: A study of the popular mind. The Macmillan Co. New York (1897)Google Scholar
  15. 15.
    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: CVPR (2010)Google Scholar
  16. 16.
    Moussaid, M., Garnier, S., Theraulaz, G., Helbing, D.: Collective information processing and pattern formation in swarms, flocks, and crowds. Topics in Cognitive Science 1(3), 469–497 (2009)CrossRefGoogle Scholar
  17. 17.
    Newman, M.E.: Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74(3), 036104 (2006)Google Scholar
  18. 18.
    Ouyang, W., Wang, X.: Single-pedestrian detection aided by multi-pedestrian detection. In: CVPR (2013)Google Scholar
  19. 19.
    Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: ICCV (2009)Google Scholar
  20. 20.
    Rabaud, V., Belongie, S.: Counting crowded moving objects. In: CVPR (2006)Google Scholar
  21. 21.
    Rodriguez, M., Ali, S., Kanade, T.: Tracking in unstructured crowded scenes. In: ICCV (2009)Google Scholar
  22. 22.
    Rodriguez, M., Laptev, I., Sivic, J., Audibert, J.Y.: Density-aware person detection and tracking in crowds. In: ICCV (2011)Google Scholar
  23. 23.
    Rodriguez, M., Sivic, J., Laptev, I., Audibert, J.Y.: Data-driven crowd analysis in videos. In: ICCV (2011)Google Scholar
  24. 24.
    Scovanner, P., Tappen, M.F.: Learning pedestrian dynamics from the real world. In: ICCV (2009)Google Scholar
  25. 25.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR (1994)Google Scholar
  26. 26.
    Shu, G., Dehghan, A., Oreifej, O., Hand, E., Shah, M.: Part-based multiple-person tracking with partial occlusion handling. In: CVPR (2012)Google Scholar
  27. 27.
    Sugimura, D., Kitani, K.M., Okabe, T., Sato, Y., Sugimoto, A.: Using individuality to track individuals: clustering individual trajectories in crowds using local appearance and frequency trait. In: CVPR (2009)Google Scholar
  28. 28.
    Tomasi, C., Kanade, T.: Detection and tracking of point features, Technical report, CMU-CS-91-132 (1991)Google Scholar
  29. 29.
    Wang, X., Ma, X., Grimson, E.: Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. PAMI 31(3), 539–555 (2009)CrossRefGoogle Scholar
  30. 30.
    Yao, R., Shi, Q., Shen, C., Zhang, Y., van den Hengel, A.: Part-based visual tracking with online latent structural learning. In: CVPR (2013)Google Scholar
  31. 31.
    Zhang, L., van der Maaten, L.: Structure preserving object tracking. In: CVPR (2013)Google Scholar
  32. 32.
    Zhang, W., Wang, X., Zhao, D., Tang, X.: Graph degree linkage: Agglomerative clustering on a directed graph. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 428–441. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  33. 33.
    Zhou, B., Tang, X., Wang, X.: Measuring crowd collectiveness. In: CVPR (2013)Google Scholar
  34. 34.
    Zhou, B., Tang, X., Wang, X.: Coherent filtering: Detecting coherent motions from crowd clutters. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 857–871. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  35. 35.
    Zhou, B., Tang, X., Wang, X.: Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents. In: CVPR (2012)Google Scholar
  36. 36.
    Zhou, B., Wang, X., Tang, X.: Random field topic model for semantic region analysis in crowded scenes from tracklets. In: CVPR (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Feng Zhu
    • 1
  • Xiaogang Wang
    • 2
    • 3
  • Nenghai Yu
    • 1
  1. 1.Department of Electronic Engineering and Information ScienceUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Department of Electronic EngineeringThe Chinese University of Hong KongHong KongChina
  3. 3.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

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