Object-Based Multiple Foreground Segmentation in RGBD Video
Object-Based Multiple Foreground Segmentation in RGBD Video
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DOI：
10.1109/tip.2017.2651369
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发表时间：
2017-03
影响因子：
10.6
通讯作者：
H. Fu;Dong Xu;Stephen Lin
中科院分区：
文献类型：
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作者：
H. Fu;Dong Xu;Stephen Lin
我们提出了一种RGB和深度（RGBD）的视频分割方法，利用深度数据，可以提取场景中的多个前景。这种视频分割被解决为在全连接图中制定的对象提议选择问题，其中可以选择灵活数量的前景。在我们的图中，每个节点代表一个建议，边则表示解决方案的帧内和帧间约束。建议的RGBD视频显着图的基础上选择，其中基于深度的功能被用来增强前景的识别。实验结果表明，本文提出的多前景分割方法优于相关技术，深度线索作为一个有益的补充RGB功能。此外，我们的方法在具有估计深度图的常规RGB视频上提供了与最先进的RGB视频分割技术相当的性能。
We present an RGB and Depth (RGBD) video segmentation method that takes advantage of depth data and can extract multiple foregrounds in the scene. This video segmentation is addressed as an object proposal selection problem formulated in a fully-connected graph, where a flexible number of foregrounds may be chosen. In our graph, each node represents a proposal, and the edges model intra-frame and inter-frame constraints on the solution. The proposals are selected based on an RGBD video saliency map in which depth-based features are utilized to enhance the identification of foregrounds. Experiments show that the proposed multiple foreground segmentation method outperforms related techniques, and the depth cue serves as a helpful complement to RGB features. Moreover, our method provides performance comparable to the state-of-the-art RGB video segmentation techniques on regular RGB videos with estimated depth maps.