An Experimental Framework for Evaluating PTZ Tracking Algorithms

  • Pietro Salvagnini
  • Marco Cristani
  • Alessio Del Bue
  • Vittorio Murino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)

Abstract

PTZ (Pan-Tilt-Zoom) cameras are powerful devices in video surveillance applications, because they offer both wide area coverage and highly detailed images in a single device. Tracking with a PTZ camera is a closed loop procedure that involves computer vision algorithms and control strategies, both crucial in developing an effective working system. In this work, we propose a novel experimental framework that allows to evaluate image tracking algorithms in controlled and repeatable scenarios, combining the PTZ camera with a calibrated projector screen on which we can play different tracking situations. We applied such setup to compare two different tracking algorithms, a kernel-based (mean-shift) tracking and a particle filter, opportunely tuned to fit with a PTZ camera. As shown in the experiments, our system allows to finely investigate pros and cons of each algorithm.

Keywords

Particle Filter Tracking Algorithm Visual Tracking Intrinsic Parameter Optical Center 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agapito, L., Hartley, R., Hayman, E.: Linear self-calibration of a rotating and zooming camera. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 (1999)Google Scholar
  2. 2.
    Bimbo, A.D., Dini, F., Grifoni, A., Pernici, F.: Uncalibrated framework for on-line camera cooperation to acquire human head imagery in wide areas. In: AVSS 2008: Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. 252–258. IEEE Computer Society, Washington, DC, USA (2008)CrossRefGoogle Scholar
  3. 3.
    Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal 2(2) (1998)Google Scholar
  4. 4.
    Cai, Y., de Freitas, N., Little, J.: Robust visual tracking for multiple targets. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 107–118. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1631–1643 (2005)CrossRefGoogle Scholar
  6. 6.
    Comaniciu, D., Ramesh, V., Meer, P., Member, S., Member, S.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)CrossRefGoogle Scholar
  7. 7.
    Davis, J., Chen, X.: Calibrating pan-tilt cameras in wide-area surveillance networks. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 144–149 (October 2003)Google Scholar
  8. 8.
    Ellis, A., Shahrokni, A., Ferryman, J.M.: Pets2009 and winter-pets 2009 results: A combined evaluation. In: Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–8 (December 2009)Google Scholar
  9. 9.
    Greiffenhagen, M., Comaniciu, D., Niemann, H., Ramesh, V.: Design, analysis, and engineering of video monitoring systems: An approach and a case study. PIEEE 89(10), 1498–1517 (2001)Google Scholar
  10. 10.
    Isard, M., Blake, A.: Condensation: Conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998), doi:10.1023/A:1008078328650CrossRefGoogle Scholar
  11. 11.
    Lieberknecht, S., Benhimane, S., Meier, P., Navab, N.: A dataset and evaluation methodology for template-based tracking algorithms. In: 8th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2009, pp. 145–151 (October 2009)Google Scholar
  12. 12.
    Ling, H., Wu, Y., Blasch, E., Chen, G., Lang, H., Bai, L.: Evaluation of visual tracking in extremely low frame rate wide area motion imagery. In: 14th Conference on Information Fusion (FUSION, 2011). IEEE, Los Alamitos (2011)Google Scholar
  13. 13.
    Raimondo, D.M., Gasparella, S., Sturzenegger, D., Lygeros, J., Morari, M.: A tracking algorithm for ptz cameras. In: 2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2010 (September 2010)Google Scholar
  14. 14.
    Varcheie, P., Bilodeau, G.-A.: People tracking using a network-based ptz camera. Machine Vision and Applications 22, 1–20 (2010), doi:10.1007/s00138-010-0300-1Google Scholar
  15. 15.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 13:1–13 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pietro Salvagnini
    • 1
  • Marco Cristani
    • 1
    • 2
  • Alessio Del Bue
    • 1
  • Vittorio Murino
    • 1
    • 2
  1. 1.Istituto Italiano di Tecnologia (IIT)GenovaItaly
  2. 2.Computer Science DepartmentUniversity of VeronaVeronaItaly

Personalised recommendations