Unsupervised video object segmentation is a crucial application in video analysis when there is no prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a video clip. In this paper, a novel unsupervised video object segmentation approach via distractor-aware online adaptation (DOA) is proposed. DOA models spatiotemporal consistency in video sequences by capturing background dependencies from adjacent frames. Instance proposals are generated by the instance segmentation network for each frame and they are grouped by motion information as positives or hard negatives. To adopt high-quality hard negatives, the block matching algorithm is then applied to preceding frames to track the associated hard negatives. General negatives are also introduced when there are no hard negatives in the sequence. The experimental results demonstrate these two kinds of negatives are complementary. Finally, we conduct DOA using positive, negative, and hard negative masks to update the foreground and background segmentation. The proposed approach achieves state-of-the-art results on two benchmark datasets, the DAVIS 2016 and the Freiburg-Berkeley motion segmentation (FBMS)-59.

当没有关于对象的先验信息时，无监督视频对象分割是视频分析中的关键应用。当多个对象出现并在视频剪辑中进行交互时，这将成为巨大的挑战。本文提出了一种新的无干扰的视频对象分割方法，即基于干扰点的在线自适应算法。DOA通过捕获相邻帧的背景依存关系来对视频序列中的时空一致性建模。实例提议是由实例分割网络为每个帧生成的，并通过运动信息将它们分为正或负。为了采用高质量的硬底片，然后将块匹配算法应用于前面的帧以跟踪关联的硬底片。当序列中没有硬底片时，也会引入一般底片。实验结果表明这两种底片是互补的。最后，我们使用正，负和硬负掩码进行DOA，以更新前景和背景分割。所提出的方法在两个基准数据集DAVIS 2016和Freiburg-Berkeley运动分割（FBMS）-59上获得了最新的结果。