目的 提出一种基于颜色特征和边缘特征相融合的算法,实现对复杂交通场景中车辆阴影的检测和消除。方法 首先,通过经典混合高斯背景建模方法建立背景模型,以帧差法获取运动目标前景。其次,针对复杂多变的交通道路场景,采用串行融合策略检测车辆阴影。对运动目标前景基于边缘特征检测阴影之后,再进行RGB颜色特征方法检测阴影,此过程中利用边缘差分、形态学处理等运算以达到更好的阴影消除效果。为提高算法效率,对前景区域进行阴影评估,从而判断是否有必要进行阴影检测和消除。结果 通过与统计参数法SP、统计非参数法SNP、两类判定性非模型法DNM1、DNM2等算法的对比,本文算法的阴影检测率和阴影识别率分别有大约10%的提升。实验结果表明,该算法能够有效消除车辆阴影,具有良好的准确性和鲁棒性。结论 本文算法结合颜色和边缘两种特征,弥补基于单个特征方法的单一性,降低由于阴影区域边缘复杂、车辆颜色与阴影颜色相近等原因造成的阴影误检率,阴影消除效果良好。

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