  Object segmentation and object tracking are fundamental research area in the
computer vision community. These two topics are diffcult to handle some common
challenges, such as occlusion, deformation, motion blur, and scale variation.
The former contains heterogeneous object, interacting object, edge ambiguity,
and shape complexity. And the latter suffers from difficulties in handling fast
motion, out-of-view, and real-time processing. Combining the two problems of
video object segmentation and tracking (VOST) can overcome their respective
difficulties and improve their performance. VOST can be widely applied to many
practical applications such as video summarization, high definition video
compression, human computer interaction, and autonomous vehicles. This article
aims to provide a comprehensive review of the state-of-the-art tracking
methods, and classify these methods into different categories, and identify new
trends. First, we provide a hierarchical categorization existing approaches,
including unsupervised VOS, semi-supervised VOS, interactive VOS, weakly
supervised VOS, and segmentation-based tracking methods. Second, we provide a
detailed discussion and overview of the technical characteristics of the
different methods. Third, we summarize the characteristics of the related video
dataset, and provide a variety of evaluation metrics. Finally, we point out a
set of interesting future works and draw our own conclusions.
