In this work, we introduce a large high-diversity database for generic object tracking, called GOT-10k. GOT-10k is backboned by the semantic hierarchy of WordNet. It populates a majority of 563 object classes and 87 motion patterns in real-world, resulting in a scale of over 10 thousand video segments and 1.5 million bounding boxes. To our knowledge, GOT-10k is by far the richest motion trajectory dataset, and its coverage of object classes is more than a magnitude wider than similar scale counterparts. By publishing GOT-10k, we hope to encourage the development of generic purposed trackers that work for a wide range of moving objects and under diverse real-world scenarios. To promote generalization and avoid the evaluation results biased to seen classes, we follow the one-shot principle in dataset splitting where training and testing classes are zero-overlapped. We also carry out a series of analytical experiments to select a compact while highly representative testing subset -- it embodies 84 object classes and 32 motion patterns with only 180 video segments, allowing for efficient evaluation. Finally, we train and evaluate a number of representative trackers on GOT-10k and analyze their performance. The evaluation results suggest that tracking in real-world unconstrained videos is far from being solved, and only 40% of frames are successfully tracked using top ranking trackers. All the dataset, evaluation toolkit and baseline results will be made available.

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。

GOT-10k: 野外通用物体追踪的大规模高多样化基准