运动目标阴影在很大程度上会影响运动目标跟踪、行为识别的正确性和有效性．为此，文中提出了一种基于混合高斯模型和马尔科夫随机场的自适应阴影检测方法．该方法首先对混合高斯模型进行改进，使其可以自适应调整参数学习率以消除浅阴影; 然后采用马尔科夫随机场综合邻域的空间依赖性信息进行精确的阴影检测．为了提高基于马尔科夫随机场的阴影检测的精度与效率，采用信息容量进行颜色特征选择，并利用基于自适应阈值的初始阴影检测结果来设定马尔科夫随机场的初始参数．实验结果表明，利用文中提出的方法检测阴影，能有效地解决阴影检测的误分类问题，提高阴影检测的准确度．

Shadows of moving objects often reduce the accuracy and the effectiveness of tracking and recognition of moving objects. In order to distinguish the objects from their shadows,an adaptive shadow detection method is proposed based on the Gaussian mixture model ( GMM) and the Markov random field ( MRF) . In this method,first,an improved GMM,which adaptively adjusts the parameter learning ratio,is proposed to remove the light shadow of
an object. Then,a new approach to shadow detection is put forward based on the spatial dependence information about the integrated neighborhood of MRF. Moreover,in order to improve the MRF-based shadow detection accuracy and effectiveness,the information capacity is used to select color features,and the results of a coarse shadow detection obtained from the adaptive threshold-based segmentation are employed to initialize the parameters of MRF. Experimental results indicate that the proposed approach effectively avoids the misclassification existing in the shadow detection and improves the detection accuracy.