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Moving vehicle shadow elimination approach based on mark growing of multi-feature fusion

机译:基于多特征融合标记增长的运动车辆阴影消除方法

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In order to eliminate vehicle shadow in moving vehicle detection, a novel vehicle shadow elimination approach is presented based on mark growing of multi-feature fusion. The selection of mark points and the establishment of growing rules are critical. Firstly, background model is obtained by mixture Gaussian method. Then gradient difference of foreground and background is calculated. Morphological method is adopted to recover approximate shadow area. Typical shadow points or mark points are got from this area. As shadow area is less bright than object area and the color inside shadow area is similar, the growing rules are established. Growing is carried out starting from mark points based on growing rules to eliminate shadows. As combining various features such as texture, brightness and color, the experiment results prove the effectiveness of this method.
机译:为了消除运动车辆检测中的车辆阴影,提出了一种基于多特征融合标记增长的新型车辆阴影消除方法。标记点的选择和增长规则的建立至关重要。首先,通过混合高斯方法获得背景模型。然后计算前景和背景的梯度差。采用形态学方法恢复近似阴影区域。典型的阴影点或标记点是从该区域获得的。由于阴影区域的亮度小于对象区域的亮度,并且阴影区域内部的颜色相似,因此建立了增长规则。从基于增长规则的标记点开始进行增长以消除阴影。通过结合纹理,亮度和颜色等多种特征,实验结果证明了该方法的有效性。

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