Moving cast shadow elimination based on luminance and texture features for traffic flow

Liang Gao, J. Xing, Hui Li, Yongzhi Wang, Lina Zheng, Xiling Luo
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Abstract

A new algorithm namely moving cast shadow elimination based on luminance and texture features (MSELT) to detect moving shadows of vehicles is investigated in this paper. Different from traditional methods only performed in color space, we combine the luminance in the CIE Luv color space and texture feature to determine shadows. The proposed algorithm based on Gaussian Mixture Model (GMM) uses the luminance weight in the CIE Luv color space to model background, do texture analysis and detect shadows. Texture analysis is performed by evaluating the gradients in the foreground with the observation that shadow regions present smooth texture characteristics. The experimental results show that this method outperforms results obtained with color space information alone, particularly in detection of vehicles which present similar luminance characteristics with shadows.
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基于亮度和纹理特征的交通流移动阴影消除
本文研究了一种基于亮度和纹理特征的运动阴影消除算法(MSELT)。与传统的仅在色彩空间中进行的方法不同,我们将CIE Luv色彩空间中的亮度与纹理特征相结合来确定阴影。该算法基于高斯混合模型(GMM),利用CIE Luv色彩空间中的亮度权重对背景进行建模、纹理分析和阴影检测。纹理分析通过评估前景的梯度,观察阴影区域呈现光滑的纹理特征。实验结果表明,该方法优于单独使用色彩空间信息获得的结果,特别是在检测与阴影具有相似亮度特征的车辆时。
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