Multiple Object Tracking Via Multi-layer Multi-modal Framework

  • Hang-Bong Kang
  • Kihong Chun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

In this paper, we propose a new multiple object tracking method via multi-layer multi-modal framework. To handle erroneous merge and labeling problem in multiple object tracking, we use a multi layer representation of dynamic Bayesian network and modified sampling method. For robust visual tracking, our dynamic Bayesian network based tracker fuses multi-modal features such as color and edge orientation histogram. The proposed method was evaluated under several real situations and promising results were obtained.

Keywords

Target Object Dynamic Bayesian Network Edge Orientation Multiple Object Tracking Label Problem 
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.

References

  1. 1.
    McCormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. Int. J. Comput. Vis (2000)Google Scholar
  2. 2.
    Isard, M., McCormick, J.: Bramble: A Bayesian multiple blob tracker. In: Proc. ICCV’01 (2001)Google Scholar
  3. 3.
    Khan, Z., Balch, T., Dellaert, F.: An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 279–290. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Yu, T., Wu, T.: Collaborative Tracking of Multiple Targets. In: Proc. CVPR’04 (2004)Google Scholar
  5. 5.
    Qu, W., Schonfeld, D., Mohamed, M.: Real-time Interactively Distributed Multi-Object Tracking Using a Magnetic-Inertia Potential Model. In: Proc. ICCV’05 (2005)Google Scholar
  6. 6.
    Yang, C., Duraiswami, R., Davis, R.: Fast Multiple Object Tracking via a Hierarchical Particle Filter. In: Proc. ICCV’05 (2005)Google Scholar
  7. 7.
    Kang, H.-B., Cho, S.-H.: A Dynamic Bayesian Network-Based Framework for Visual Tracking. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 603–610. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int’l J. Computer Vision, 91–110 (2004)Google Scholar
  9. 9.
    Nummiaro, K., Koller-Meier, E., Van Gool, L.: A Color-Based Particle Filter. In: First International Workshop on Generative-Model-Based Vision, pp. 53–60 (2002)Google Scholar
  10. 10.
    Intel Open Source Probabilistic Network Library (OpnePNL), http://www.intel.com/research/mrl/pnl

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Hang-Bong Kang
    • 1
  • Kihong Chun
    • 1
  1. 1.Dept. of Computer Eng., Catholic University of Korea, #43-1 Yokkok 2-dong Wonmi-Gu, Puchon, Kyonggi-DoKorea

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