从单个移动视图中查看足球运动员的个别位置
Adrien Maglo1, Astrid Orcesi1, Julien Denize1
1Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究提出了一个新的框架,用于从广播视频中提取足球运动员的位置. 该方法准确地跟踪球员并估计他们在球场上的实时位置,优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 运动分析 运动分析
- 机器学习 机器学习
背景情况:
- 球员的位置数据对于团队体育中的表现评估至关重要.
- 从广播视频中提取这些数据需要有效的跟踪方法.
- 现有的方法经常在动态摄像头视图和玩家识别方面扎.
研究的目的:
- 开发一个强大的框架来从广播视频中提取个人足球运动员的位置.
- 为了提高运动分析中的球员跟踪和场地注册的准确性和效率.
- 提供一种可靠的方法来生成用于性能分析的球员位置数据.
主要方法:
- 一种体育场登记方法估计了使用编码器-解码器架构与视觉变压器的pitch-to-frame同谱.
- 一个多人追踪器使用双边匹配,交叉-跨-联盟和距离标准来生成轨道.
- 一个重新识别模型,在自我监督下微调,将基于外观的轨道合并为玩家识别.
主要成果:
- 拟议的框架准确地估计了球场上关键点的位置.
- 体育场地注册和2D球员追踪方法的表现优于以前对公共数据集的研究.
- 这款2D玩家追踪器在2022年和2023年的SoccerNet追踪挑战中获得了第一名.
结论:
- 开发的框架为从广播体育视频中追踪球员和提取位置数据提供了有效的解决方案.
- 该方法在准确地定位球员在球场上的方面表现出卓越的性能.
- 这一进步对体育分析和绩效评估有重大影响.
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