View-based adaptive affine tracking

  • Fernando de la Torre
  • Shaogang Gong
  • Stephen McKenna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)


We propose a model for view-based adaptive affine tracking of moving objects. We avoid the need for feature-based matching in establishing correspondences through learning landmarks. We use an effective bootstrapping process based on colour segmentation and selective attention. We recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate our approach.


Singular Value Decomposition Training Image Gabor Wavelet Global Illumination Affine Parameter 
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 1998

Authors and Affiliations

  • Fernando de la Torre
    • 1
  • Shaogang Gong
    • 2
  • Stephen McKenna
    • 3
  1. 1.Department of Signal TheoryUniversitat Ramon LlullSpain
  2. 2.Department of Computer ScienceQueen Mary and Westfield CollegeEngland
  3. 3.Department of Applied ComputingUniversity of DundeeScotland

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