  With more and more large-scale datasets available for training, visual
tracking has made great progress in recent years. However, current research in
the field mainly focuses on tracking generic objects. In this paper, we present
TSFMO, a benchmark for \textbf{T}racking \textbf{S}mall and \textbf{F}ast
\textbf{M}oving \textbf{O}bjects. This benchmark aims to encourage research in
developing novel and accurate methods for this challenging task particularly.
TSFMO consists of 250 sequences with about 50k frames in total. Each frame in
these sequences is carefully and manually annotated with a bounding box. To the
best of our knowledge, TSFMO is the first benchmark dedicated to tracking small
and fast moving objects, especially connected to sports. To understand how
existing methods perform and to provide comparison for future research on
TSFMO, we extensively evaluate 20 state-of-the-art trackers on the benchmark.
The evaluation results exhibit that more effort are required to improve
tracking small and fast moving objects. Moreover, to encourage future research,
we proposed a novel tracker S-KeepTrack which surpasses all 20 evaluated
approaches. By releasing TSFMO, we expect to facilitate future researches and
applications of tracking small and fast moving objects. The TSFMO and
evaluation results as well as S-KeepTrack are available at
\url{https://github.com/CodeOfGithub/S-KeepTrack}.
