We address a method of pedestrian segmentation in a video in a spatio-temporally consistent way. For this purpose, given a bounding box sequence of each pedestrian obtained by a conventional pedestrian detector and tracker, we construct a spatio-temporal graph on a video and segment each pedestrian on the basis of a well-established graph-cut segmentation framework. More specifically, we consider three terms as an energy function for the graph-cut segmentation: (1) a data term, (2) a spatial pairwise term, and (3) a temporal pairwise term. To maintain better temporal consistency of segmentation even under relatively large motions, we introduce a transportation minimization framework that provides a temporal correspondence. Moreover, we introduce the edge-sticky superpixel to maintain the spatial consistency of object boundaries. In experiments, we demonstrate that the proposed method improves segmentation accuracy indices, such as the average and weighted intersection of union on TUD datasets and the PETS2009 dataset at both the instance level and semantic level.

中文翻译：

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基于时空一致图割和最优运输的行人分割

我们以时空一致的方式解决视频中行人分割的方法。为此，给定由常规行人检测器和跟踪器获得的每个行人的包围盒序列，我们在视频上构建时空图，并在完善的图割分割框架的基础上对每个行人进行分割。更具体地说，我们将三个项视为图割分割的能量函数：（1）数据项，（2）空间成对项，（3）时间成对项。为了即使在相对较大的运动下也能保持更好的分割时间一致性，我们引入了一个运输最小化框架来提供时间上的对应关系。此外，我们引入了边缘粘性超像素以保持对象边界的空间一致性。在实验中