Improving video segmentation algorithms by detection of and adaption to altered illumination

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Abstract

Changing illumination constitutes a serious challenge for video segmentation
algorithms, especially in outdoor scenes under cloudy conditions.
Rapid illumination changes, e.g. caused by varying cloud cover,
often cause existing segmentation algorithms to erroneously classify
large parts of the image as foreground.
Here a method that extends existing segmentation algorithms by
detecting illumination changes using a CUSUM detector and adjusting
the background model to conform with the new illumination is
presented. The method is shown to work for two segmentation algorithms,
and it is indicated how the method could be extended to other
algorithms.
Original languageEnglish
Number of pages20
JournalPreprints in Mathematical Sciences
Volume2008:9
Publication statusUnpublished - 2008

Subject classification (UKÄ)

  • Mathematics
  • Probability Theory and Statistics

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