论文标题
视频的新动作识别框架突出显示体育赛事中的摘要
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events
论文作者
论文摘要
迄今为止，在体育活动中广泛实施了视频中人类行动识别的机器学习。尽管一些研究过去已经成功，但精度仍然是最重要的问题。在这项研究中，我们提出了一个高准确的框架，该框架通过使用基于两个经典的开源结构（即Yolo-V3和OpenPose）的三级预测算法来自动剪辑运动视频流。已经发现，通过使用适度的体育视频训练数据，我们的方法可以准确地执行体育活动突出显示。与以前的系统相比，我们的方法在准确性上显示出一些优势。这项研究可能是一种新的剪裁系统，以扩展视频摘要在运动领域的潜在应用，并促进匹配分析系统的发展。
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.
