Spatio-Temporal Clustering Model for Multi-object Tracking through Occlusions

  • Lei Zhang
  • Qing Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


The occlusion in dynamic or clutter scene is a critical issue in multi-object tracking. Using latent variable to formulate this problem, some methods achieved state-of-the-art performance, while making an exact solution computationally intractable. In this paper, we present a hierarchical association framework to address the problem of occlusion in a complex scene taken by a single camera. At the first stage, reliable tracklets are obtained by frame-to-frame association of detection responses in a flow network. After that, we propose to formulate tracklets association problem in a spatio-temporal clustering model which presents the problem as faithfully as possible. Due to the important role that affinity model plays in our formulation, we then construct a sparsity induced affinity model under the assumption that a detection sample in a tracklet can be efficiently represented by another tracklet belonging to the same object. Furthermore, we give a near-optimal algorithm based on globally greedy strategy to deal with spatio-temporal clustering, which runs linearly with the number of tracklets. We quantitatively evaluate the performance of our method on three challenging data sets and achieve a significant improvement compared to state-of-the-art tracking systems.


Greedy Algorithm Pedestrian Detector Association Problem Greedy Solution Clutter 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amberg, B., Vetter, T.: Graphtrack: Fast and globally optimal tracking in videos. In: CVPR, pp. 1209–1216 (2011)Google Scholar
  2. 2.
    Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)Google Scholar
  3. 3.
    Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: CVPR, pp. 1265–1272 (2011)Google Scholar
  4. 4.
    Andriyenko, A., Roth, S., Schindler, K.: An analytical formulation of global occlusion reasoning for multi-target tracking. In: ICCV Workshops, pp. 1839–1846 (2011)Google Scholar
  5. 5.
    Andriyenko, A., Schindler, K., Roth, S.: Discrete-continuous optimization for multi-target tracking. In: CVPR (2012)Google Scholar
  6. 6.
    Berclaz, J., Fleuret, F., Türetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1806–1819 (2011)CrossRefGoogle Scholar
  7. 7.
    Cheng, H., Liu, Z., Yang, J.: Sparsity induced similarity measure for label propagation. In: ICCV, pp. 317–324 (2009)Google Scholar
  8. 8.
    Ess, A., Leibe, B., Gool, L.J.V.: Depth and appearance for mobile scene analysis. In: ICCV, pp. 1–8 (2007)Google Scholar
  9. 9.
    Felzenszwalb, P.F., McAllester, D.A., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: CVPR (2008)Google Scholar
  10. 10.
    Huang, C., Wu, B., Nevatia, R.: Robust Object Tracking by Hierarchical Association of Detection Responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788–801. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Kuo, C.H., Nevatia, R.: How does person identity recognition help multi-person tracking? In: CVPR, pp. 1217–1224 (2011)Google Scholar
  12. 12.
    Leibe, B., Schindler, K., Gool, L.J.V.: Coupled detection and trajectory estimation for multi-object tracking. In: ICCV, pp. 1–8 (2007)Google Scholar
  13. 13.
    Pets, benchmark dataset (2009),
  14. 14.
    Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR, pp. 1201–1208 (2011)Google Scholar
  15. 15.
    Stalder, S., Grabner, H., Van Gool, L.: Cascaded Confidence Filtering for Improved Tracking-by-Detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 369–382. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)Google Scholar
  17. 17.
    Wu, B., Nevatia, R.: Tracking of multiple, partially occluded humans based on static body part detection. In: CVPR, vol. (1), pp. 951–958 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lei Zhang
    • 1
  • Qing Wang
    • 1
  1. 1.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anP.R. China

Personalised recommendations