People Tracking Algorithm for Human Height Mounted Cameras

  • Vladimir Kononov
  • Vadim Konushin
  • Anton Konushin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6835)

Abstract

We present a new people tracking method for human height mounted camera, e.g. the one attached near information or advertising stand. We use state-of-the-art particle filter approach and improve it by explicitly modeling of object visibility which makes the method able to cope with difficult object overlapping. We employ our own method based on online-boosting classifiers to resolve occlusions and show that it is well suited for tracking multiple objects. In addition to training an online-classifier which is updated each frame we propose to store object appearance and update it with a certain lag. It helps to correctly handle situations when a person enters the scene while another one leaves it at the same time. We demonstrate the perfomance of our algorithm and advantages of our contributions on our own video dataset.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Andriyenko, A., Schindler, K.: Globally optimal multi-target tracking on a hexagonal lattice. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 466–479. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Berclaz, J., Fleuret, F., Fua, F.: Robust people tracking with global trajectory optimization. In: CVPR (2006)Google Scholar
  3. 3.
    Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV (2009)Google Scholar
  4. 4.
    Bugeau, A., Perez, P.: Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts. ACM J. on Image and Video Processing (2008)Google Scholar
  5. 5.
    Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: CVPR (2008)Google Scholar
  6. 6.
    Fortmann, T., Shalom, Y.B., Scheffe, M.: Sonar tracking of multiple targets using joint probabilistic data association. IEEE J. Oceanic Engineering 8(3), 173–184 (1983)CrossRefGoogle Scholar
  7. 7.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR (2006)Google Scholar
  8. 8.
    Hue, C., Le Cadre, J.P., Perez, P.: Sequential monte carlo methods for multiple target tracking and data fusion. IEEE Tr. Signal Processing 50(1), 309–325 (2002)CrossRefGoogle Scholar
  9. 9.
    Isard, M., MacCormick, J.: Bramble: a bayesian multiple-blob tracker. In: ICCV (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)CrossRefGoogle Scholar
  11. 11.
    Kang, J., Cohen, I., Medioni, G.: Tracking people in crowded scenes across multiple cameras. In: ACCV (2004)Google Scholar
  12. 12.
    Khan, S., Shah, M.: Tracking people in presence of occlusion. In: ACCV (2000)Google Scholar
  13. 13.
    Kuhn, H.: The hungarian method for solving the assignment problem. Naval Research Logistics Quart. 2, 83–97 (1955)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Huang, C., Nevatia, R.: Learning to associate: Hybridboosted multi-target tracker for crowded scene. In: CVPR (2009)Google Scholar
  15. 15.
    Li, Z., Yuan, L., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)Google Scholar
  16. 16.
    Mitzel, D., Horbert, E., Ess, A., Leibe, B.: Multi-person tracking with sparse detection and continuous segmentation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 397–410. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Okuma, K., Taleghani, A., de Freitas, N., 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
  18. 18.
    Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. Automatic Control 24(6), 843–854 (1979)CrossRefGoogle Scholar
  19. 19.
    Song, B., Jeng, T.-Y., Staudt, E., Roy-Chowdhury, A.K.: A stochastic graph evolution framework for robust multi-target tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 605–619. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM J. Computing Surveys 38(4) (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vladimir Kononov
    • 1
  • Vadim Konushin
    • 1
  • Anton Konushin
    • 1
  1. 1.Graphics & Media LabMoscow State UniversityRussia

Personalised recommendations