用模板匹配与线性分配识别足球阵型和球员位置。
EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignment
- 基于预设阵型模板，通过最小化距离匹配球员与位置。
- 支持单帧及长时段分析，可处理10秒至5分钟片段。
- 加入稳定性参数，避免微小变化导致阵型频繁切换。
理解球队阵型和球员位置对足球战术分析至关重要。本文提出一种灵活的足球阵型识别与球员位置分配方法——EFPI，该方法利用预设的静态阵型模板，结合时空跟踪数据，通过线性和分配最小化实际球员位置与模板位置之间的总距离，从而最优匹配球员到阵型位置，并选择成本最低的阵型。为提高准确性，将实际球员位置按宽高比例缩放以匹配模板尺寸。该方法在单帧上有效，亦可自然扩展至完整比赛时段、控球序列或特定时间区间（如10秒、5分钟等）。此外，引入可选稳定性参数，在相邻时段分配成本差异较小时防止阵型无意义切换。代码已开源，可通过unravelsports Python包获取。
原文摘要 · Abstract (English)
Understanding team formations and player positioning is crucial for tactical analysis in football (soccer). This paper presents a flexible method for formation recognition and player position assignment in football using predefined static formation templates and cost minimization from spatiotemporal tracking data, called EFPI. Our approach employs linear sum assignment to optimally match players to positions within a set of template formations by minimizing the total distance between actual player locations and template positions, subsequently selecting the formation with the lowest assignment cost. To improve accuracy, we scale actual player positions to match the dimensions of these formation templates in both width and length. While the method functions effectively on individual frames, it extends naturally to larger game segments such as complete periods, possession sequences or specific intervals (e.g. 10 second intervals, 5 minute intervals etc.). Additionally, we incorporate an optional stability parameter that prevents unnecessary formation changes when assignment costs differ only marginally between time segments. EFPI is available as open-source code through the unravelsports Python package.
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