On-line selection of discriminative tracking features

We present a method for evaluating multiple feature spaces while tracking, and for adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. We develop an online feature selection mechanism based on the two-class variance ratio measure, applied to log likelihood distributions computed with respect to a given feature from samples of object and background pixels. This feature selection mechanism is embedded in a tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented to illustrate how the method adapts to changing appearances of both tracked object and scene background.

• Publication year

  2003

• Venue

  Proceedings Ninth IEEE International Conference on Computer Vision

• Publication date

  2003-10-13

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/ICCV.2003.1238365
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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