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.