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This paper presents an algorithm for background modeling and foreground detection that uses scaling coefficients, which are defined with a new color model called LRGB (Lightness-Red-Green-Blue). They are employed to compare two images by finding pixels with scaled lightness. Three backgrounds are used: verified background with pixels that are considered as background, testing background with pixels that are tested several times to check if they belong to the background, and final background that is a combination of the testing and verified background (the testing background is used in places where the verified background is not defined). If a testing background pixel matches pixels from previous frames (the match is tested using scaling coefficients), it is copied to the verified background, otherwise the pixel is set as the weighted average of the corresponding pixels of the last input images. After the background is computed, foreground objects are detected by using the scaling coefficients and additional criteria. The algorithm was evaluated using the SABS Dataset, Wallflower Dataset and a subset of the CDnet 2014 Dataset. The average F measure and sensitivity with the SABS Dataset were 0.7109 and 0.8725 respectively. In the Wallflower Dataset, the total number of errors was 5280 and the total F-measure was 0.9089. In the CDnet 2014 Dataset, the F-measure for the baseline test case was 0.8887 and for the shadow test case was 0.8300.
This article was published in the following journal.
Name: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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