论文标题
UnOvost：无监督的离线视频对象细分和跟踪
UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking
论文作者
论文摘要
我们解决了无监督的视频对象细分（UVO），这是在视频序列中自动生成准确对象的准确像素掩码的任务，并在时间上始终如一地跟踪这些对象，而无需跟踪对象的任何输入。为了解决此任务，我们将Unovost（无监督的离线视频对象细分和跟踪）作为一种简单且通用的算法，能够跟踪和分割各种各样的对象。该算法在一个数字阶段中构建了轨道，首先将段分组为短时空一致的短轨迹，然后根据其视觉相似性将这些曲目合并为长期一致的对象轨道。为了实现这一目标，我们介绍了一种新型的基于轨道的森林路径切割数据关联算法，该算法在将这种森林切成构成长期一致的物体轨迹的路径之前，建立了轨道假设的决策森林。在评估我们在戴维斯2017年无监督数据集上的方法时，我们获得了最先进的性能，其平均J＆F得分为67.9％，在测试DEV上为58％，在测试范围的基准中获得56.4％，在戴维斯（Davis）2019年无需提供视频对象对象群体挑战挑战中获得了戴维斯（Davis）的第一名。即使没有给出任何应跟踪和细分对象的输入，Unovost甚至通过许多半监督视频对象分割算法进行了竞争性能。
We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixel masks for salient objects in a video sequence and of tracking these objects consistently through time, without any input about which objects should be tracked. Towards solving this task, we present UnOVOST (Unsupervised Offline Video Object Segmentation and Tracking) as a simple and generic algorithm which is able to track and segment a large variety of objects. This algorithm builds up tracks in a number stages, first grouping segments into short tracklets that are spatio-temporally consistent, before merging these tracklets into long-term consistent object tracks based on their visual similarity. In order to achieve this we introduce a novel tracklet-based Forest Path Cutting data association algorithm which builds up a decision forest of track hypotheses before cutting this forest into paths that form long-term consistent object tracks. When evaluating our approach on the DAVIS 2017 Unsupervised dataset we obtain state-of-the-art performance with a mean J &F score of 67.9% on the val, 58% on the test-dev and 56.4% on the test-challenge benchmarks, obtaining first place in the DAVIS 2019 Unsupervised Video Object Segmentation Challenge. UnOVOST even performs competitively with many semi-supervised video object segmentation algorithms even though it is not given any input as to which objects should be tracked and segmented.
