Journal
JOURNAL OF MATHEMATICAL IMAGING AND VISION
Volume 64, Issue 5, Pages 463-477Publisher
SPRINGER
DOI: 10.1007/s10851-022-01080-4
Keywords
Eigenbackground; Background modeling; Background subtraction; Principal component analysis; Gaussian mixture model; Video analysis
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This paper discusses the common technique of using dominant eigenvectors for background modeling and proposes an alternative solution by utilizing the weakest eigenvectors for better results.
Using dominant eigenvectors for background modeling (usually known as Eigenbackground) is a common technique in the literature. However, its results suffer from noticeable artifacts. Thus, there have been many attempts to reduce the artifacts by making some improvements/enhancements in the Eigenbackground algorithm. In this paper, we show the main problem of the Eigenbackground is at its own core and in fact, it may not be a good idea to use the strongest eigenvectors for modeling the background. Instead, we propose an alternative solution by exploiting the weakest eigenvectors (which are usually thrown away and treated as garbage data) for background modeling. MATLAB codes are available at the GitHub of the paper.
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