Effective Appearance Model and Similarity Measure for Particle Filtering and Visual Tracking

  • Hanzi Wang
  • David Suter
  • Konrad Schindler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)


In this paper, we adaptively model the appearance of objects based on Mixture of Gaussians in a joint spatial-color space (the approach is called SMOG). We propose a new SMOG-based similarity measure. SMOG captures richer information than the general color histogram because it incorporates spatial layout in addition to color. This appearance model and the similarity measure are used in a framework of Bayesian probability for tracking natural objects. In the second part of the paper, we propose an Integral Gaussian Mixture (IGM) technique, as a fast way to extract the parameters of SMOG for target candidate. With IGM, the parameters of SMOG can be computed efficiently by using only simple arithmetic operations (addition, subtraction, division) and thus the computation is reduced to linear complexity. Experiments show that our method can successfully track objects despite changes in foreground appearance, clutter, occlusion, etc.; and that it outperforms several color-histogram based methods.


Similarity Measure Target Object Particle Filter Color Histogram Visual Tracking 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hanzi Wang
    • 1
  • David Suter
    • 1
  • Konrad Schindler
    • 1
  1. 1.Institute for Vision Systems Engineering, Department of Electrical and Computer Systems EngineeringMonash UniversityClaytonAustralia

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