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作 者:张攀 郭文鹏 金泽军 Zhang Pan;Guo Wenpeng;Jin Zejun(School of Artificial Intelligence,Neijiang Normal University,Neijiang 641100,China)
机构地区:[1]内江师范学院人工智能学院,四川内江641100 [2]重庆邮电大学通信与信息工程学院,重庆400065
出 处:《体育科技文献通报》2023年第10期211-217,共7页Bulletin of Sport Science & Technology
基 金:内江师范学院大学生创新创业训练计划资助(项目编号:X2021011)。
摘 要:篮球竞技视频分析,是球队赛前、赛中、赛后的重要工作之一。传统方法主要以视频回放结合人工辅助方式进行球员标注、数据统计、技战术分析等,效率相对较低。近年来众多学者逐步利用深度学习技术研究实现智能化篮球竞技视频分析。本文对2012至2023年,尤其是近五年间发表于三大检索平台和中国知网的文献进行梳理,共筛选获取51篇相关文献。结果发现:最新篮球竞技视频分析系统主要区分为球场检测、球员轨迹追踪、球员识别、动作事件检测、技战术策略识别等五个模块。这些模块的性能都随着其关键技术与深度神经网络模型的融合而得到改进,对篮球竞技的辅助效果日趋明显,但也受限于高质量数据集缺乏、神经网络模型多样、多模块分离等问题,智能化水平仍有待进一步提升。Basketball competitive video analysis is one of the important tasks for a team before,during,and after the game.Traditional methods primarily rely on video playback in conjunction with manual intervention for player annotation,data statistics,technical and tactical analysis,which is relatively inefficient.Consequently,in recent years,numerous scholars have increasingly turned to deep learning technology to explore and accomplish intelligent analysis of competitive basketball videos.From 2012 to 2023,especially in the past five years,literature published in the Web of Science(WOS)and China National Knowledge Infrastructure(CNKI)was sorted out,and a total of 51 relevant articles were selected.It was found that the latest basketball competitive video analysis system is mainly divided into five modules:court detection,player trajectory tracking,player recognition,action event detection,and technical and tactical strategy recognition.The performance of these modules has been enhanced through the integration of key technologies and deep neural network models,leading to their growing impact on basketball competitions.However,the lack of high-quality datasets,diversity in neural network models,and compartmentalization of multiple modules continue to present challenges,indicating that the level of intelligence still requires further advancement.
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