We introduce here a large tracking database that offers an unprecedentedly
wide coverage of common moving objects in the wild, called GOT-10k.
Specifically, GOT-10k is built upon the backbone of WordNet structure and it
populates the majority of over 560 classes of moving objects and 87 motion
patterns, magnitudes wider than the most recent similar-scale counterparts. The
contributions of this paper are summarized in the following: (1) GOT-10k offers
over 10,000 video segments with more than 1.5 million manually labeled bounding
boxes, enabling unified training and stable evaluation of deep trackers. (2)
GOT-10k is by far the first video trajectory dataset that uses the semantic
hierarchy of WordNet to guide class population. (3) For the first time, GOT-10k
introduces the one-shot protocol for tracker evaluation, where the training and
test classes are zero-overlapped. The protocol avoids biased evaluation results
towards familiar objects and it promotes generalization in tracker development.
(4) We conduct extensive tracking experiments with 39 typical tracking
algorithms on GOT-10k and analyze their results in this paper. (5) Finally, we
develop a comprehensive platform for the tracking community that offers
full-featured evaluation toolkits, an online evaluation server, and a
responsive leaderboard. The annotations of GOT-10k's test data are kept private
to avoid tuning parameters on it. The database, toolkits, evaluation server and
baseline results are available at this http URL

本文介绍了一个基于 WordNet 结构的大型跟踪数据库，名为 GOT-10k。GOT-10k 可以提供超过 10,000 个视频段，1.5 百万个手动标记的边界框，由此可以进行深度跟踪的统一训练和稳定评估。此外，该数据库还引入了一种名为 one-shot protocol 的跟踪器评估方法，可以促进跟踪器的发展。我们在 GOT-10k 上进行了广泛的跟踪实验，并分析了实验结果。