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.

翻译：迄今为止,在体育活动中广泛采用视频机学来识别人的动作,虽然过去有一些研究取得了成功,但精确性仍然是最重要的关切。在本研究中,我们提出了一个高度精确的框架,通过使用基于两个传统开放源码结构(即YOLO-v3和OpenPose)的三级预测算法,自动剪辑体育视频流。发现通过使用少量体育视频培训数据,我们的方法可以准确地剪辑体育活动的亮点。与以往的系统相比,我们的方法显示出一些精度的优势。这项研究可以作为一种新的剪辑系统,扩大视频拼凑在体育领域的潜在应用,并促进匹配分析系统的发展。