A novel algorithm that combines color feature and edge information is proposed to detect and remove vehicle shadows in complex traffic scenes. First

a background model is built with the classical Gaussian mixture background modeling method

and the moving vehicle foreground is obtained through frame difference. Second

a serial fusion strategy that combines color feature and edge information is applied to detect and eliminate vehicle shadows. Based on vehicle shadow detection by edge information method of the moving target foreground

the RGB color feature detection method is implemented to detect the shadow area further and to obtain a precise result. Edge difference and morphological processing methods are used during the operations to detect and eliminate shadows effectively. Shadow assessment is periodically evaluated on the foreground area to improve the efficiency of the algorithm by determining the necessity of applying the proposed algorithm. By comparision with SP、 SNP、 DNM1 and DNM2 algorithm

the proposed method realizes about 10% advance on shadow detection rate and shadow recogmition rate. The high accuracy and robustness of the proposed shadow removal method are confirmed by the test results

and the effectiveness of the method is validated. The proposed method that combines color feature and edge information outperforms those based on a single feature because of their unicity. In addition

the false detection rate caused by complex edges in shadow regions and color similarity between vehicles and shadows is effectively decreased.