Due to recent advances in technology, the recording and analysis of video
data has become an increasingly common component of athlete training
programmes. Today it is incredibly easy and affordable to set up a fixed camera
and record athletes in a wide range of sports, such as diving, gymnastics,
golf, tennis, etc. However, the manual analysis of the obtained footage is a
time-consuming task which involves isolating actions of interest and
categorizing them using domain-specific knowledge. In order to automate this
kind of task, three challenging sub-problems are often encountered: 1)
temporally cropping events/actions of interest from continuous video; 2)
tracking the object of interest; and 3) classifying the events/actions of
interest.
Most previous work has focused on solving just one of the above sub-problems
in isolation. In contrast, this paper provides a complete solution to the
overall action monitoring task in the context of a challenging real-world
exemplar. Specifically, we address the problem of diving classification. This
is a challenging problem since the person (diver) of interest typically
occupies fewer than 1% of the pixels in each frame. The model is required to
learn the temporal boundaries of a dive, even though other divers and
bystanders may be in view. Finally, the model must be sensitive to subtle
changes in body pose over a large number of frames to determine the
classification code. We provide effective solutions to each of the sub-problems
which combine to provide a highly functional solution to the task as a whole.
The techniques proposed can be easily generalized to video footage recorded
from other sports.

该研究提供了一个实现了运动员训练视频自动化分析的完整解决方案，结合特定的挑战性真实例子，着重解决了跳水分类监测等关键问题，其中包涵运动员训练、视频分析、动作分类、跳水监测以及其他运动录像等广泛运用。