提出了一种新颖的利用随机森林检测深度图像中遮挡现象的方法。该方法从一幅深度图像中提取每个像素点的遮挡相关特征，利用随机森林分类器检测每个像素点是否为遮挡边界点，得到图像中的遮挡边界。主要贡献在于：提出了一种新的遮挡相关特征深度值离散度特征，同时引入高斯曲率特征，并将它们与现有特征相结合来检测遮挡边界；以特征重要性和特征提取时间为衡量标准，对深度图像中的各遮挡相关特征进行了分析评估，在此基础上，选取平均深度差、最大深度差、平均曲率、高斯曲率和深度值离散度5种特征用于设计遮挡检测分类器；一种新的遮挡检测方法，利用随机森林解决深度图像的遮挡检测问题。实验结果表明，同已有方法相比，所提方法具有较高的准确性和较好的通用性。

A novel occlusion detection approach is proposed for depth image by using Random Forest. The occlusion related features of each pixel in the depth image are extracted, and then the Random Forest classifier is used for detecting whether each pixel is an occlusion boundary point or not. All the occlusion boundaries in the input depth image are obtained. This work is distinguished by three contributions. A new occlusion related feature named depth dispersion is proposed and the Gaussian curvature feature is introduced, and both of them are used for occlusion detection by combining with other features. All the occlusion related features in depth image are analyzed and evaluated by using the importance and extraction time as criterion. On this basis, five features such as average depth difference, maximal depth difference, mean curvature, Gaussian curvature and depth dispersion are selected for designing the occlusion detection classifier. A new occlusion detection approach takes the Random Forest to solve occlusion detection problem in depth image. The experimental results show that, compared with the existing methods, the proposed approach has higher accuracy and better generality.