Manually labeling video datasets for segmentation tasks is extremely time consuming. In this paper, we introduce ScribbleBox, a novel interactive framework for annotating object instances with masks in videos. In particular, we split annotation into two steps: annotating objects with tracked boxes, and labeling masks inside these tracks. We introduce automation and interaction in both steps. Box tracks are annotated efficiently by approximating the trajectory using a parametric curve with a small number of control points which the annotator can interactively correct. Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy. Segmentation masks are corrected via scribbles which are efficiently propagated through time. We show significant performance gains in annotation efficiency over past work. We show that our ScribbleBox approach reaches 88.92% J&F on DAVIS2017 with 9.14 clicks per box track, and 4 frames of scribble annotation.

本文介绍了一种交互式的注释框架 ScribbleBox，它将注释分为两个步骤：使用跟踪框注释对象实例，并在这些轨迹内标记掩码，我们在两个步骤中引入了自动化和交互性，并且使用我们的方法，在 DAVIS2017 上达到了 88.92％ J＆F，每个框轨迹使用 9.14 个点击和 4 帧的涂鸦注释。

ScribbleBox：视频对象分割交互式标注框架