智能交通系统希望从交通视频中获取车辆的相关信息来管理交通流，而视频中车辆阴影的检测和移除是移动车辆检测的一个关键问题。首先提出一种新颖的背景模型更新方法，它是基于从前帧学习到的目标知识来更新背景模型；其次提出一种新的颜色空间，VIRGBC颜色空间，进行车辆阴影分割，它是通过从不同颜色空间中提取对阴影敏感的光谱特征和几何特征进行融合获得VIRGBC颜色特征；然后分别对VIRGBC颜色空间中各通道利用条件随机场进行训练，利用调和平均数合并获得分割结果。实验结果表明，该方法的precision\|recall曲线下的面积为93.5%，比现有其他算法获得更好的结果。

Intelligent Transportation System research aims at managing traffic flow by providing vehicle information in the scene.Moreover，moving vehicle detection in a video sequence is a very difficult question because of vehicle shadows.In this paper，first，a novel background model update is proposed，which uses object knowledge which is learnt by previous frame to renew background model.Second，a new color space is proposed for vehicle shadow segmentation，which is VIRGBC color space.The proposed technique exploits spectral and geometrical properties of shadows，by fusing all the shadow sensitive features extracted from different color spaces to obtain the VIRGBC color feature.Finally，we use conditional random model to train each channel mask of the VIRGBC color space，and fuse every channel result to obtain the segmentation result by harmonious mean.Experimental results demonstrate the effectiveness of the proposed method，and particularly interesting are the results obtained by the new approach in terms of area under precision recall curve (93.5%)，which is better than those obtained by other state-of-the-art method.