GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
复制标题
GOT-10k：用于野外通用对象跟踪的大型高多样性基准
DOI：
10.1109/tpami.2019.2957464
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发表时间：
2021-05-01
影响因子：
23.6
通讯作者：
Huang, Kaiqi
中科院分区：
文献类型：
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作者：
Huang, Lianghua;Zhao, Xin;Huang, Kaiqi
我们在这里介绍一个大型跟踪数据库，它提供了一个前所未有的广泛覆盖的常见移动对象在野外，称为GOT-10 k。具体来说，GOT-10 k建立在WordNet结构的基础上[1]，它填充了超过560类移动对象和87种运动模式中的大多数，其幅度比最近的类似规模的同行更宽[19]，[20]，[23]，[26]。通过发布大型高多样性数据库，我们的目标是为开发类无关的通用短期跟踪器提供统一的训练和评估平台。GOT-10 k的特点和本文的贡献总结如下。(1)GOT-10 k提供超过10，000个视频片段，超过150万个手动标记的边界框，实现了深度跟踪器的统一训练和稳定评估。(2)GOT-10 k是迄今为止第一个使用WordNet的语义层次结构来指导类填充的视频轨迹数据集，这确保了对各种移动对象的全面和相对公正的覆盖。(3)GOT-10 k首次引入了用于跟踪器评估的一次性协议，其中训练和测试类零重叠。该协议避免了对熟悉的对象有偏见的评价结果，它促进了跟踪器开发的泛化。(4)GOT-10 k提供了额外的标签，如运动类和对象可见率，促进了运动感知和遮挡感知跟踪器的开发。(5)本文对39种典型的跟踪算法及其变种在GOT-10 k上进行了广泛的跟踪实验，并对实验结果进行了分析。(6)最后，我们为跟踪社区开发了一个全面的平台，提供功能齐全的评估工具包，在线评估服务器和响应式排行榜。GOT-10 k测试数据的注释是私有的，以避免对其进行参数调整。
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 [1] 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 [19], [20], [23], [26]. By releasing the large high-diversity database, we aim to provide a unified training and evaluation platform for the development of class-agnostic, generic purposed short-term trackers. The features of GOT-10k and the contributions of this article 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, which ensures a comprehensive and relatively unbiased coverage of diverse moving objects. (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) GOT-10k offers additional labels such as motion classes and object visible ratios, facilitating the development of motion-aware and occlusion-aware trackers. (5) We conduct extensive tracking experiments with 39 typical tracking algorithms and their variants on GOT-10k and analyze their results in this paper. (6) 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.