Privacy Preserving Multi-target Tracking

  • Anton MilanEmail author
  • Stefan Roth
  • Konrad Schindler
  • Mineichi Kudo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9010)


Automated people tracking is important for a wide range of applications. However, typical surveillance cameras are controversial in their use, mainly due to the harsh intrusion of the tracked individuals’ privacy. In this paper, we explore a privacy-preserving alternative for multi-target tracking. A network of infrared sensors attached to the ceiling acts as a low-resolution, monochromatic camera in an indoor environment. Using only this low-level information about the presence of a target, we are able to reconstruct entire trajectories of several people. Inspired by the recent success of offline approaches to multi-target tracking, we apply an energy minimization technique to the novel setting of infrared motion sensors. To cope with the very weak data term from the infrared sensor network we track in a continuous state space with soft, implicit data association. Our experimental evaluation on both synthetic and real-world data shows that our principled method clearly outperforms previous techniques.


Sensor Response Infrared Sensor Surveillance Camera Indoor Scenario Multiple Hypothesis Tracker 
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.

Supplementary material

Supplementary material (mp4 9,982 KB)


  1. 1.
    Babaguchi, N., Koshimizu, T., Umata, I., Toriyama, T.: Psychological study for designing privacy protected video surveillance system: PriSurv. In: Senior, A. (ed.) Protecting Privacy in Video Surveillance, pp. 147–164. Springer, London (2009)CrossRefGoogle Scholar
  2. 2.
    Norris, C., Armstrong, G.: CCTV and the social structuring of surveillance. In: Painter, K., Tilley, N. (eds.) Surveillance of Public Space. Crime Prevention Studies, vol. 10, pp. 157–178. Criminal Justice Press, Monsey (1999)Google Scholar
  3. 3.
    Jiang, H., Fels, S., Little, J.J.: A linear programming approach for multiple object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)Google Scholar
  4. 4.
    Milan, A., Roth, S., Schindler, K.: Continuous energy minimization for multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36, 58–72 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008)Google Scholar
  6. 6.
    Hosokawa, T., Kudo, M., Nonaka, H., Toyama, J.: Soft authentication using an infrared ceiling sensor network. Pattern Anal. Appl. 12, 237–249 (2009)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Luo, X., Shen, B., Guo, X., Luo, G., Wang, G.: Human tracking using ceiling pyroelectric infrared sensors. In: 2009 IEEE International Conference on Control and Automation, ICCA 2009, pp. 1716–1721 (2009)Google Scholar
  8. 8.
    Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24, 843–854 (1979)CrossRefGoogle Scholar
  9. 9.
    Fortmann, T.E., Bar-Shalom, Y., Scheffe, M.: Multi-target tracking using joint probabilistic data association. In: 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, vol. 19, pp. 807–812 (1980)Google Scholar
  10. 10.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82, 35–45 (1960)CrossRefGoogle Scholar
  11. 11.
    Andriyenko, A., Schindler, K., Roth, S.: Discrete-continuous optimization for multi-target tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
  12. 12.
    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
  13. 13.
    Butt, A.A., Collins, R.T.: Multi-target tracking by Lagrangian relaxation to min-cost network flow. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)Google Scholar
  14. 14.
    Tao, S., Kudo, M., Nonaka, H.: Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network. Sensors 12, 16920–16936 (2012)CrossRefGoogle Scholar
  15. 15.
    Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008)Google Scholar
  16. 16.
    Roshan Zamir, A., Dehghan, A., Shah, M.: GMCP-tracker: global multi-object tracking using generalized minimum clique graphs. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 343–356. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  17. 17.
    Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Robust tracking-by-detection using a detector confidence particle filter. In: IEEE International Conference on Computer Vision (ICCV) (2009)Google Scholar
  18. 18.
    Milan, A., Schindler, K., Roth, S.: Challenges of ground truth evaluation of multi-target tracking. In: Proceedings of the CVPR 2013 Workshop on Ground Truth - What is a Good Dataset? (2013)Google Scholar
  19. 19.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. Image Video Process. 2008, 1–10 (2008)CrossRefGoogle Scholar
  20. 20.
    Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Anton Milan
    • 1
    Email author
  • Stefan Roth
    • 2
  • Konrad Schindler
    • 3
  • Mineichi Kudo
    • 4
  1. 1.School of Computer ScienceUniversity of AdelaideAdelaideAustralia
  2. 2.Department of Computer ScienceTU DarmstadtDarmstadtGermany
  3. 3.Photogrammetry and Remote SensingETH ZürichZürichSwitzerland
  4. 4.Division of Computer ScienceHokkaido UniversitySapporoJapan

Personalised recommendations