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Pathnodes Integration of Standalone Particle Filters for People Tracking on Distributed Surveillance Systems

  • Roberto Vezzani
  • Davide Baltieri
  • Rita Cucchiara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

In this paper, we present a new approach to object tracking based on batteries of particle filter working in multicamera systems with non overlapped fields of view. In each view the moving objects are tracked with independent particle filters; each filter exploits a likelihood function based on both color and motion information. The consistent labeling of people exiting from a camera field of view and entering in a neighbor one is obtained sharing particles information for the initialization of new filtering trackers. The information exchange algorithm is based on path-nodes, which are a graph-based scene representation usually adopted in computer graphics. The approach has been tested even in case of simultaneous transitions, occlusions, and groups of people. Promising results have been obtained and here presented using a real setup of non overlapped cameras.

Keywords

Pathnode multicamera tracking 

References

  1. 1.
    Calderara, S., Cucchiara, R., Prati, A.: Bayesian-competitive consistent labeling for people surveillance. IEEE Trans. on PAMI 30, 354–360 (2008)CrossRefGoogle Scholar
  2. 2.
    Khan, S., Shah, M.: Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Trans. on PAMI 25, 1355–1360 (2003)CrossRefGoogle Scholar
  3. 3.
    Li, J., Chua, C., Ho, Y.: Color based multiple people tracking. In: Proc. of IEEE Intl Conf. on Control, Automation, Robotics and Vision, pp. 309–314 (2002)Google Scholar
  4. 4.
    Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M., Shafer, S.: Multi-camera multi-person tracking for easyliving. In: Proc. of IEEE Intl Workshop on Visual Surveillance, pp. 3–10 (2000)Google Scholar
  5. 5.
    Kang, J., Cohen, I., Medioni, G.: Continuous tracking within and across camera streams. In: Proc. of IEEE Int’l Conference on Computer Vision and Pattern Recognition., vol. 1, pp. 267–272 (2003)Google Scholar
  6. 6.
    Chang, S., Gong, T.H.: Tracking multiple people with a multi-camera system. In: Proc. of IEEE Workshop on Multi-Object Tracking, pp. 19–26 (2001)Google Scholar
  7. 7.
    Yue, Z., Zhou, S., Chellappa, R.: Robust two-camera tracking using homography. In: Proc. of IEEE Intl Conf. on Acoustics, Speech, and Signal Processing, pp. 1–4 (2004)Google Scholar
  8. 8.
    Arulampalam, S., Maskell, S., Gordon, N.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50, 174–188 (2002)CrossRefGoogle Scholar
  9. 9.
    Doucet, A.: On sequential simulation-based methods for Bayesian filtering. Technical report (1998)Google Scholar
  10. 10.
    Li, A., Jing, Z., Hu, S.: Robust observation model for visual tracking in particle filter. AEU - International Journal of Electronics and Communications 61, 186–194 (2007)CrossRefGoogle Scholar
  11. 11.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts and shadows in video streams. IEEE Trans. on PAMI 25, 1337–1342 (2003)CrossRefGoogle Scholar
  12. 12.
    Feller, W.: An Introduction to Probability Theory and Its Applications, vol. 1. Wiley, Chichester (1968)zbMATHGoogle Scholar
  13. 13.
    Roullot, E.: A unifying framework for color image calibration. In: Proc. of IWSSIP 2008, pp. 97–100 (2008)Google Scholar
  14. 14.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly, Cambridge (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Roberto Vezzani
    • 1
  • Davide Baltieri
    • 1
  • Rita Cucchiara
    • 1
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversity of Modena and Reggio EmiliaModenaItaly

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