Gaussian mixture model in improved HLS color space for human silhouette extraction

Authors:
Nurul Arif Setiawan;Hong Seok-Ju;Kim Jang-Woon;Lee Chil-Woo
Affiliations:
Intelligent Media Lab, Department of Computer Engineering, Chonnam National University, Gwangju, Korea;Intelligent Media Lab, Department of Computer Engineering, Chonnam National University, Gwangju, Korea;Intelligent Media Lab, Department of Computer Engineering, Chonnam National University, Gwangju, Korea;Intelligent Media Lab, Department of Computer Engineering, Chonnam National University, Gwangju, Korea
Venue:
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Year:
2006

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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.