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Pattern Analysis and Applications

, Volume 7, Issue 2, pp 144–150 | Cite as

Improving the selection of feature points for tracking

  • Zoran ŽivkovićEmail author
  • Ferdinand van der Heijden
Theoretical Advances

Abstract

The problem considered in this paper is how to select the feature points (in practice, small image patches are used) in an image from an image sequence, such that they can be tracked adequately further through the sequence. Usually, the tracking is performed by some sort of local search method looking for a similar patch in the next image in the sequence. Therefore, it would be useful if we could estimate “the size of the convergence region” for each image patch. There is a smaller chance of error when calculating the displacement for an image patch with a large convergence region than for an image patch with a small convergence region. Consequently, the size of the convergence region can be used as a proper goodness measure for a feature point. For the standard Kanade-Lucas-Tomasi (KLT) tracking method, we propose a simple and fast way to approximate the convergence region for an image patch. In the experimental part, we test our hypothesis on a large set of real data.

Keywords

Feature (interest) point selection Motion estimation Visual tracking Optical flow Convergence region Robustness 

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Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  1. 1.Laboratory for Measurement and InstrumentationUniversity of TwenteEnschedeThe Netherlands
  2. 2.University of AmsterdamAmsterdamThe Netherlands

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