发表机构
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究旨在解决篮球视频理解问题，提出以球员为中心的BasketEvent数据集，引入PlayNet推理框架，通过建模多种互动并聚合时间证据进行事件预测，实验证明其在体育视频理解上优于基线。
AI 中文摘要
全面的篮球视频理解不仅需要确定发生了什么事件，还需要确定谁对此负责以及关键证据何时出现。然而，现有方法通常将空间感知和语义识别视为孤立任务，无法将事件与单个球员联系起来，也无法在复杂的集体动态中确定其时间边界。为弥合这一差距，我们引入了BasketEvent，这是一个从真实NBA广播中整理出的以球员为中心的篮球事件理解数据集。在BasketEvent中，事件标签与负责的球员相关联，并提供了一个包含1000个样本的手动注释子集，带有精确的事件间隔，以评估时间证据定位。基于此数据，我们提出了PlayNet，这是一个以球员为中心的推理框架，它将篮球视频映射到带有时间证据的球员级事件预测。具体来说，PlayNet跟踪关键实体，关联球员身份，并通过对球员-球员、球员-球和全球球场互动进行建模来推理事件，同时通过门控池聚合稀疏的时间证据。大量实验表明，PlayNet显著优于代表性的视频级和基于裁剪的基线，证明了以球员为中心的建模在细粒度体育视频理解方面的优越性。我们的数据、代码和模型将公开可用。
英文摘要
Comprehensive basketball video understanding requires resolving not only what event occurs, but also who is responsible and when the key evidence appears. However, exist- ing methods typically treat spatial perception and semantic recognition as isolated tasks, failing to ground events to individual players or pinpoint their temporal boundaries within complex collective dynamics. To bridge this gap, we introduce BasketEvent, a player- centric basketball event understanding dataset curated from real NBA broadcasts. In BasketEvent, event labels are grounded to the responsible players, and a manually an- notated subset of 1,000 samples with precise event intervals is provided to evaluate tem- poral evidence localization. Based on this data, we propose PlayNet, a player-centric reasoning framework that maps basketball videos to player-level event predictions with temporal evidence. Concretely, PlayNet tracks key entities, associates player identities, and reasons about events by modeling player-player, player-ball, and global court inter- actions, while aggregating sparse temporal evidence via gated pooling. Extensive experi- ments demonstrate that PlayNet significantly outperforms representative video-level and crop-based baselines, proving the superiority of player-centric modeling for fine-grained sports video understanding. Our data, code, and models will be made publicly available.
