Improving Video Segmentation Algorithms by Detection of and Adaption to Altered Illumination

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.