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帕德尔Tracker100:一个数据集用于智能玩家和球跟踪在帕德尔运动中的球

Roberto Bada-Nerin¹, Paula Rodríguez-González¹, Denis Kreibel¹

¹NTT Data, Spain.

|February 25, 2026

概括

帕德尔Tracker100是一个新的,大数据集,用于帕德尔分析,拥有10万,详细注释用于追踪球,球员姿势和射击事件. 本资源旨在推进用于体育分析的AI驱动解决方案.

科学领域:

计算机视觉计算机视觉
深度学习 (Deep Learning) 是一种深度学习.
运动分析运动分析

背景情况:

体育分析中的监督学习模型需要高质量的注释数据集.
帕德尔运动缺乏全面的,预先注释的数据集,用于大规模分析.
现有的技术受到AI模型培训数据稀缺的限制.

研究的目的:

介绍PadelTracker100,这是一个新的,大规模的注释数据集,用于滑板运动.
提供一个基准数据集,以克服滑板运动分析的局限性.
促进人工智能驱动的解决方案的进展,用于球和跨体育应用.

主要方法:

从2022年世界羽毛球巡回赛 (WPT) 决赛的两场比赛中获取的数据,分辨率为1920 × 1080@30.
使用半自动管道与代YOLO训练来追踪球的轨迹.
采用ViTPose-L用于玩家姿势估计,并为镜头事件识别注释了40,135.

主要成果:

开发了PadelTracker100,拥有近10万和全面的注释.
标注包括球轨迹,球员位置,球员姿势和六种类型的射击事件.
通过彻底的手动改进过程,确保了高质量的注释.

结论:

帕德尔Tracker100解决了帕德尔运动分析中的关键数据缺口.
该数据集将促进战术评估,伤害预防和实时视频处理等领域的研究.
允许开发人工智能驱动的工具,用于羽毛球和潜在的跨体育应用.

关键词:

球探测器球探测器计算机视觉计算机视觉计算机视觉位置估计位置估计射击事件的发生.运动分析分析.这里是ViTPose的位置.这是一个YOLO YOLO.

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