In traffic surveillance video analysis systems, the cast shadows of vehicles often have a negative effect on video analysis results. A novel cast shadow detection framework, which consists of a new foreground detection method and a cast shadow detection method, is presented in this paper to detect and remove the cast shadows from the foreground. The new foreground detection method applies an innovative Global Foreground Modeling (GFM) method, a Gaussian mixture model or GMM, and the Bayes classifier for foreground and background classification. While the GFM method is for global foreground modeling, the GMM is for local background modeling, and the Bayes classifier applies both the foreground and the background models for foreground detection. The rationale of the GFM method stems from the observation that the foreground objects often appear in recent frames and their trajectories often lead them to different locations in these frames. As a result, the statistical models used to characterize the foreground objects should not be pixel based or locally defined. The cast shadow detection method contains four hierarchical steps. First, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the HSV color space. Second, a new shadow region detection method is proposed to cluster the candidate shadow pixels into shadow regions. Third, a statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented for classifying shadow pixels. Fourth, an aggregated shadow detection method is presented for final shadow detection. Experiments using the public video data ‘Highway-1’ and ‘Highway-3’, and the New Jersey Department of Transportation (NJDOT) real traffic video sequences show the feasibility of the proposed method. In particular, the proposed method achieves better shadow detection performance than the popular shadow detection methods, and is able to improve the traffic video analysis results.

在交通监控视频分析系统中，车辆的阴影经常对视频分析结果产生负面影响。提出了一种新颖的投射阴影检测框架，该框架由一种新的前景检测方法和一种投射阴影检测方法组成，用于检测和消除前景中的投射阴影。新的前景检测方法应用了创新的全局前景建模（GFM）方法，高斯混合模型或GMM以及用于前景和背景分类的贝叶斯分类器。GFM方法用于全局前景建模，而GMM方法用于局部背景建模，并且Bayes分类器将前景模型和背景模型都应用于前景检测。GFM方法的原理源于以下观察结果：前景对象经常出现在最近的帧中，并且它们的轨迹经常将它们引导到这些帧中的不同位置。结果，用于表征前景对象的统计模型不应基于像素或局部定义。投射阴影检测方法包含四个层次步骤。首先，提出了一组新的色度标准以检测HSV颜色空间中的候选阴影像素。其次，提出了一种新的阴影区域检测方法，将候选阴影像素聚类为阴影区域。第三，提出了一种统计阴影模型，该模型使用单个高斯分布对阴影类别进行建模，以对阴影像素进行分类。第四，提出了一种用于最终阴影检测的聚合阴影检测方法。使用公共视频数据“ Highway-1”和“ Highway-3”以及新泽西州交通局（NJDOT）的真实交通视频序列进行的实验证明了该方法的可行性。特别地，所提出的方法具有比流行的阴影检测方法更好的阴影检测性能，并且能够改善交通视频分析结果。