为了检测视频序列中的遮挡边界，提出一种新颖的基于无监督在线学习的遮挡边界检测方法。该方法提取视频序列中待测帧的遮挡相关特征并计算其对应的时间长度，利用对冲算法思想并结合时间长度及不同遮挡特征求得待测帧中像素点的遮挡相关信息，利用各特征的遮挡相关信息进行投票，完成当前帧图像的遮挡边界检测。利用Online Boosting 思想以当前帧的检测结果来估计下一帧的特征投票权重，实现后续帧图像的遮挡边界检测。该方法通过在线学习思想改变不同特征的权重完成遮挡边界检测功能，无需预先获取视频序列的先验知识。实验结果表明，同已有方法相比，该方法具有较高的准确性和较好的通用性。

In order to detect occlusion boundary in video sequences, a novel occlusion boundary detection approach based on unsupervised online learning is proposed. The occlusion related features of the frame to be detected in video sequences are extracted and the time length corresponding to the frame is calculated. The pixel points' occlusion related information in the frame to be detected is obtained using Hedge algorithm and combining time length with different occlusion features. The occlusion related information of different features is voted to accomplish occlusion boundary detection of current frame. The detection result of current frame is used to estimate the feature weight for next frame based on Online Boosting idea to realize the detection of subsequent frames. The proposed method changes the weight of different features through online learning idea to accomplish occlusion boundary detection, and does not need to obtain priori knowledge of video sequences in advance. Experimental results show that, compared with existing methods, the proposed method has higher accuracy and better generality.