Single object tracking (SOT) research falls into a cycle -- trackers perform well on most benchmarks but quickly fail in challenging scenarios, causing researchers to doubt the insufficient data content and take more effort to construct larger datasets with more challenging situations. However, inefficient data utilization and limited evaluation methods more seriously hinder SOT research. The former causes existing datasets can not be exploited comprehensively, while the latter neglects challenging factors in the evaluation process. In this article, we systematize the representative benchmarks and form a Single Object Tracking metaverse (SOTVerse) -- a user-defined SOT task space to break through the bottleneck. We first propose a 3E Paradigm to describe tasks by three components (i.e., environment, evaluation, and executor). Then, we summarize task characteristics, clarify the organization standards, and construct SOTVerse with 12.56 million frames. Specifically, SOTVerse automatically labels challenging factors per frame, allowing users to generate user-defined spaces efficiently via construction rules. Besides, SOTVerse provides two mechanisms with new indicators and successfully evaluates trackers under various subtasks. Consequently, SOTVerse first provides a strategy to improve resource utilization in the computer vision area, making research more standardized and scientific. The SOTVerse, toolkit, evaluation server, and results are available at http://metaverse.aitestunion.com.

翻译：单目标跟踪（SOT）研究陷入了一个循环——跟踪器在大多数基准测试中表现良好，但在具有挑战性的场景中迅速失效，导致研究者怀疑数据内容不足，并投入更多精力构建包含更多挑战性场景的更大规模数据集。然而，低效的数据利用和有限的评估方法更严重地阻碍了SOT研究。前者导致现有数据集无法被全面利用，而后者则在评估过程中忽略了具有挑战性的因素。本文系统梳理了具有代表性的基准测试，并构建了一个单目标跟踪元宇宙（SOTVerse）——一种用户定义的SOT任务空间，以突破这一瓶颈。我们首先提出了一种3E范式，通过三个组件（即环境、评估和执行器）来描述任务。随后，我们总结了任务特征，明确了组织标准，并构建了包含1256万帧的SOTVerse。具体而言，SOTVerse自动标注每帧的挑战性因素，使用户能够通过构建规则高效生成自定义空间。此外，SOTVerse提供了两种带有新指标的机制，成功评估了跟踪器在各种子任务下的性能。因此，SOTVerse首次提供了一种提升计算机视觉领域资源利用率的策略，使研究更加标准化和科学化。SOTVerse、工具包、评估服务器及结果可在http://metaverse.aitestunion.com获取。