IEEE Trans Image Process. 2017 Mar;26(3):1418-1427. doi: 10.1109/TIP.2017.2651369. Epub 2017 Jan 10.
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.
我们提出了一种利用深度数据并能从场景中提取多个前景的 RGB 和深度（RGBD）视频分割方法。该视频分割被视为一个完全连接的图中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，每个节点代表一个提议，边模型对解决方案的帧内和帧间约束。提议是基于 RGBD 视频显著图选择的，其中基于深度的特征用于增强对前景的识别。实验表明，所提出的多前景分割方法优于相关技术，并且深度线索可作为 RGB 特征的有益补充。此外，我们的方法在具有估计深度图的常规 RGBD 视频上的性能可与最先进的 RGBD 视频分割技术相媲美。
