Multiple Target Tracking in World Coordinate with Single, Minimally Calibrated Camera

  • Wongun Choi
  • Silvio Savarese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)


Tracking multiple objects is important in many application domains. We propose a novel algorithm for multi-object tracking that is capable of working under very challenging conditions such as minimal hardware equipment, uncalibrated monocular camera, occlusions and severe background clutter. To address this problem we propose a new method that jointly estimates object tracks, estimates corresponding 2D/3D temporal trajectories in the camera reference system as well as estimates the model parameters (pose, focal length, etc) within a coherent probabilistic formulation. Since our goal is to estimate stable and robust tracks that can be univocally associated to the object IDs, we propose to include in our formulation an interaction (attraction and repulsion) model that is able to model multiple 2D/3D trajectories in space-time and handle situations where objects occlude each other. We use a MCMC particle filtering algorithm for parameter inference and propose a solution that enables accurate and efficient tracking and camera model estimation. Qualitative and quantitative experimental results obtained using our own dataset and the publicly available ETH dataset shows very promising tracking and camera estimation results.


Camera Motion Camera Parameter Camera Model Single Camera Structure From Motion 
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.
    Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR (2008)Google Scholar
  2. 2.
    Hoiem, D., Efros, A., Hebert, M.: Putting objects in perspective. In: CVPR (2006)Google Scholar
  3. 3.
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. PAMI (2002)Google Scholar
  5. 5.
    Avidan, S.: Ensemble tracking. PAMI (2007)Google Scholar
  6. 6.
    Yin, Z., Collins, R.: On-the-fly object modeling while tracking. In: CVPR (2007)Google Scholar
  7. 7.
    Matthews, I., Ishikawa, T., Baker, S.: The template update problem. PAMI 26, 810–815 (2004)Google Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  9. 9.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. In: PAMI (2009)Google Scholar
  10. 10.
    Okuma, K., Taleghani, A., Freitas, N.D., Freitas, O.D., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors (2007)Google Scholar
  12. 12.
    Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV (2009)Google Scholar
  13. 13.
    Khan, Z., Balch, T., Dellaert, F.: Mcmc-based particle filtering for tracking a variable number of interacting targets (2005)Google Scholar
  14. 14.
    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
  15. 15.
    Scovanner, P., Tappen, M.: Learning pedestrian dynamics from the real world. In: ICCV (2009)Google Scholar
  16. 16.
    Tomasi, C., Kanade, T.: Detection and tracking of point features. In: Carnegie Mellon University Technical Report (1991)Google Scholar
  17. 17.
    Kuhn, H.W.: The hungarian method for the assignment problem. In: Naval Research Logistics Quarterly (1955)Google Scholar
  18. 18.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: Real-time single camera slam. PAMI 29, 1052–1067 (2007)Google Scholar
  19. 19.
    Smith, P., Reid, I., Davison, A.: Real-time monocular slam with straight lines. In: BMVC (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wongun Choi
    • 1
  • Silvio Savarese
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of MichiganAnn ArborUSA

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