We approach video object segmentation (VOS) by splitting the task into two
sub-tasks: bounding box level tracking, followed by bounding box segmentation.
Following this paradigm, we present BoLTVOS (Box-Level Tracking for VOS), which
consists of an R-CNN detector conditioned on the first-frame bounding box to
detect the object of interest, a temporal consistency rescoring algorithm, and
a Box2Seg network that converts bounding boxes to segmentation masks. BoLTVOS
performs VOS using only the firstframe bounding box without the mask. We
evaluate our approach on DAVIS 2017 and YouTube-VOS, and show that it
outperforms all methods that do not perform first-frame fine-tuning. We further
present BoLTVOS-ft, which learns to segment the object in question using the
first-frame mask while it is being tracked, without increasing the runtime.
BoLTVOS-ft outperforms PReMVOS, the previously best performing VOS method on
DAVIS 2016 and YouTube-VOS, while running up to 45 times faster. Our bounding
box tracker also outperforms all previous short-term and longterm trackers on
the bounding box level tracking datasets OTB 2015 and LTB35. A newer version of
this work can be found at arXiv:1911.12836.

本文提出了一种从边界框级别追踪开始的视频对象分割方法，该方法包括一个基于 R-CNN 的检测器、一个时间一致性重新评分算法，以及将边界框转换为分割掩模的 Box2Seg 网络。在 DAVIS 2017 和 YouTube-VOS 数据集上的实验表明，该方法优于所有未进行第一帧微调的方法。此外，该方法还得到了优化，可在不增加运行时间的情况下学习跟踪过程中对象的分割，且运行速度比先前最好的 VOS 方法快达 45 倍。