Department of Physics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
Postgraduate School, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
Comput Intell Neurosci. 2022 Mar 15;2022:6118798. doi: 10.1155/2022/6118798. eCollection 2022.
With the explosive growth of sports video data on the internet platform, how to scientifically manage this information has become a major challenge in the current big data era. In this context, a new lightweight player segmentation algorithm is proposed to realize the automatic analysis of basketball game video. Firstly, semantic events are expressed by extracting group and global motion features. A complete basketball game video is divided into three stages, and a basketball event classification method integrating global group motion patterns and domain knowledge is proposed. Secondly, a player segmentation algorithm based on lightweight deep learning is proposed to detect basketball players, segment the players, and finally extract players' spatial features based on deep learning to realize players' pose estimation. As the experimental results indicate, when a proposed 2-stage classification algorithm is used to classify the videos, the accuracy of identifying layup, the shooting, and other 2-pointers are improved by 21.26% and 6.41%, respectively. And the accuracy of average events sees an improvement of 2.74%. The results imply that the 2-stage classification based on event-occ is effective. After comparing the four methods of classifying players, it is found that there is no significant difference among these four methods about the accuracy of segmenting. Nevertheless, when judged with the time that these methods take separately, FCN-CNN (Fully Convolutional Network-Convolutional Neural Network) based on superpixels has overwhelming advantages. The event analysis method of basketball game video proposed here can realize the automatic analysis of basketball video, which is beneficial to promoting the rapid development of basketball and even sports.
随着互联网平台上运动视频数据的爆炸式增长，如何科学地管理这些信息已经成为当前大数据时代的一大挑战。在此背景下，提出了一种新的轻量级球员分割算法，以实现篮球比赛视频的自动分析。首先，通过提取组和全局运动特征来表示语义事件。将完整的篮球比赛视频分为三个阶段，并提出了一种结合全局组运动模式和领域知识的篮球事件分类方法。其次，提出了一种基于轻量级深度学习的球员分割算法，用于检测篮球运动员、分割运动员，最后基于深度学习提取运动员的空间特征，实现运动员的姿势估计。实验结果表明，当使用所提出的 2 阶段分类算法对视频进行分类时，上篮、投篮等 2 分球的识别准确率分别提高了 21.26%和 6.41%，平均事件的准确率提高了 2.74%。这表明基于事件发生的 2 阶段分类是有效的。在比较了 4 种球员分类方法后，发现这 4 种方法在分割准确率上没有显著差异。然而，当单独判断这些方法所花费的时间时，基于超像素的 FCN-CNN（全卷积网络-卷积神经网络）具有绝对优势。这里提出的篮球比赛视频事件分析方法可以实现篮球视频的自动分析，这有利于促进篮球乃至体育事业的快速发展。
