BMASH：基于球运动感知的足球头球检测
BMASH: Ball-Motion-Aware Soccer Header Spotting
- University of Jyväskylä（于韦斯屈莱大学）
- Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出BMASH框架，融合Video Swin动作识别与球检测特征，提升足球头球检测性能，在片段级指标上优于基线，并在全视频检测中实现可比的F1性能。
AI中文摘要：
计算机视觉的最新进展使得广播体育视频在事件分析、性能评估和球员安全应用方面越来越有用。然而，在足球领域，由于头球事件的微妙和短暂特性，头球检测仍然是一个具有挑战性的问题。本文聚焦于足球头球检测：识别广播视频中球接触球员头部的时刻。我们首先将Video Swin改编并评估为这项任务的强动作识别基线，然后引入BMASH，一种融合检测器派生球特征的球运动感知融合框架。BMASH将Video Swin的动作表示与来自帧级足球检测的球存在和运动特征相结合，将球员动作上下文与球动力学相集成，以区分头球与视觉上相似的事件。我们使用游戏级划分（包含独立的测试比赛和轮换验证折）评估BMASH，同时考虑中心窗口分类和连续全视频检测。结果表明，Video Swin为头球检测提供了强基线，而BMASH在片段级AP和ROC-AUC上优于相应的Video Swin基线。在连续全视频检测中，BMASH实现了可比较的事件级F1性能，但具有不同的精确率-召回率权衡。
英文摘要：
Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety applications. In soccer, however, header spotting remains a challenging problem due to the subtle and short-lived nature of header events. This paper focuses on soccer header spotting: identifying moments in broadcast videos where the ball contacts a player's head. We first adapt and evaluate Video Swin as a strong action-recognition baseline for this task, and then introduce BMASH, a ball-motion-aware fusion framework that integrates detector-derived ball features. BMASH combines Video Swin action representations with ball-presence and motion features from frame-level soccer-ball detection, integrating player-action context with ball dynamics to distinguish headers from visually similar events. We evaluate BMASH using game-level splits with separate test matches and rotating validation folds, considering both centered-window classification and continuous full-video spotting. Results show that Video Swin provides a strong baseline for header spotting, while BMASH improves clip-level AP and ROC-AUC over the corresponding Video Swin baseline. In continuous full-video spotting, BMASH achieves a comparable event-level F1-performance with a different precision--recall trade-off.
