  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.
