Abstract
In this paper, we present an algorithm using Gaussian Mixture Model (GMM) for foreground segmentation which can differentiate shadow region from objects with simple criteria. In the algorithm, we have utilized the Improved HLS (IHLS) color space model as the fundamental description for image, instead of using raw RGB data. IHLS color space has an advantage over the standard RGB space to recognize shadow region from object by utilizing luminance and saturation-weighted hue information directly, without any calculation of chrominance and luminance. By exploiting this feature in GMM, we obtain adaptive background model with good sensitivity to color changes and shadow.
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Setiawan, N.A., Seok-Ju, H., Jang-Woon, K., Chil-Woo, L. (2006). Gaussian Mixture Model in Improved HLS Color Space for Human Silhouette Extraction. In: Pan, Z., Cheok, A., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds) Advances in Artificial Reality and Tele-Existence. ICAT 2006. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941354_76
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DOI: https://doi.org/10.1007/11941354_76
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