  To date, machine learning for human action recognition in video has been
widely implemented in sports activities. Although some studies have been
successful in the past, precision is still the most significant concern. In
this study, we present a high-accuracy framework to automatically clip the
sports video stream by using a three-level prediction algorithm based on two
classical open-source structures, i.e., YOLO-v3 and OpenPose. It is found that
by using a modest amount of sports video training data, our methodology can
perform sports activity highlights clipping accurately. Comparing with the
previous systems, our methodology shows some advantages in accuracy. This study
may serve as a new clipping system to extend the potential applications of the
video summarization in sports field, as well as facilitates the development of
match analysis system.
