Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
Implementation provided by the authors.
References
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)
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)
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)
Milan, A., Roth, S., Schindler, K.: Continuous energy minimization for multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36, 58–72 (2014)
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)
Hosokawa, T., Kudo, M., Nonaka, H., Toyama, J.: Soft authentication using an infrared ceiling sensor network. Pattern Anal. Appl. 12, 237–249 (2009)
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)
Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24, 843–854 (1979)
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)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82, 35–45 (1960)
Andriyenko, A., Schindler, K., Roth, S.: Discrete-continuous optimization for multi-target tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
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)
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)
Tao, S., Kudo, M., Nonaka, H.: Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network. Sensors 12, 16920–16936 (2012)
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)
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)
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)
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)
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. Image Video Process. 2008, 1–10 (2008)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material (mp4 9,982 KB)
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Milan, A., Roth, S., Schindler, K., Kudo, M. (2015). Privacy Preserving Multi-target Tracking. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-16634-6_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16633-9
Online ISBN: 978-3-319-16634-6
eBook Packages: Computer ScienceComputer Science (R0)