用球员射门前动作预测足球点球方向，准确率达63.9%。
Predicting Soccer Penalty Kick Direction Using Human Action Recognition
- 结合动作识别特征与上下文信息建模射门前动作
- 最高准确率63.9%，超越真实守门员决策表现
- 适用于体育场景中的行为预判研究
动作预判已成为人体动作识别（HAR）领域的热点，但其在真实体育场景中的应用受限于高质量标注数据的缺乏。本文构建了一个手动标注的足球点球数据集，旨在基于射门前球员动作预测射门方向。提出一种深度学习分类器，融合HAR特征嵌入与上下文元数据进行建模。在七个架构族（MViTv2、MViTv1、SlowFast、Slow、X3D、I3D、C2D）的22个主干模型上评估，预测左右方向的准确率最高达63.9%，优于真实守门员的判断表现。结果验证了该数据集在预期动作识别中的价值，并证明所提模型可推广至其他体育预测任务。
原文摘要 · Abstract (English)
Action anticipation has become a prominent topic in Human Action Recognition (HAR). However, its application to real-world sports scenarios remains limited by the availability of suitable annotated datasets. This work presents a novel dataset of manually annotated soccer penalty kicks to predict shot direction based on pre-kick player movements. We propose a deep learning classifier to benchmark this dataset that integrates HAR-based feature embeddings with contextual metadata. We evaluate twenty-two backbone models across seven architecture families (MViTv2, MViTv1, SlowFast, Slow, X3D, I3D, C2D), achieving up to 63.9% accuracy in predicting shot direction (left or right), outperforming the real goalkeepers' decisions. These results demonstrate the dataset's value for anticipatory action recognition and validate our model's potential as a generalizable approach for sports-based predictive tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。