为了准确检测视频序列中的遮挡边界，提出了一种利用随机森林分类器检测遮挡边界的方法。该方法首先分割视频序列某一帧的边缘检测结果得到超像素和超像素边缘，并将超像素边缘分解为多个短的直线段。然后结合表观、运动和边缘结构信息提取每个直线段的遮挡相关特征并组合成特征向量，将此特征向量输入遮挡边界分类器检测每个直线段是否为遮挡边界。最后可视化所有确定为遮挡边界的直线段得到该帧图像的遮挡边界检测结果。实验结果表明所提方法具有较高的准确性。%To detect the occlusion boundary in video sequences accurately,an occlusion boundary detection approach based on random forests classifier is proposed. Firstly,the edges of current frame in a video are segmented to obtain superpixels and superpixel edges,and then the superpixels edges are decomposed into short line fragments. Secondly,the occlusion related features of each line fragment are extracted by combining appearance,motion and edge structure cues and the extracted features are assembled to feature vector. After that,the feature vector of each line fragment is inputted to the occlusion boundary classifier to detect whether each line fragment is an occlusion boundary or not. Finally,the occlusion boundary of the current frame in a video is obtained by visualizing all the line fragments which belong to occlusion boundary. The experimental results show that the proposed approach has higher accuracy.
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