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Gaussian Mixture Model in Improved HLS Color Space for Human Silhouette Extraction

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, instea...

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Main Authors:	Setiawan, Nurul Arif, Seok-Ju, Hong, Jang-Woon, Kim, Chil-Woo, Lee
Format:	Conference Proceeding
Language:	English
Subjects:	Applied sciences Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology foreground segmentation Gaussian Mixture Model Improved HLS Software
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creator	Setiawan, Nurul Arif Seok-Ju, Hong Jang-Woon, Kim Chil-Woo, Lee
description	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.
doi_str_mv	10.1007/11941354_76
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Applied sciences Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology foreground segmentation Gaussian Mixture Model Improved HLS Software
title	Gaussian Mixture Model in Improved HLS Color Space for Human Silhouette Extraction
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