聚焦篮球视频中谁在何时做了什么，实现球员级事件理解
BasketEvent: Understanding Who Did What and When in Basketball Videos
- 以球员为中心建模，融合球员-球员、球员-球互动关系
- 在1000个标注样本上实现精准事件时间定位，准确率显著提升
- 适合体育视频分析、智能裁判辅助等场景的研究者
篮球视频理解需同时明确事件内容、责任球员及关键证据出现时间。现有方法常将空间感知与语义识别割裂，难以将事件关联到具体球员或精确定位其时间边界。为此，我们构建了BasketEvent数据集，源自真实NBA转播，事件标签均锚定至责任球员，并提供1000个手动标注样本，包含精确的事件时间段，用于评估时间证据定位。基于此，我们提出PlayNet框架，通过追踪关键实体、关联球员身份，结合球员-球员、球员-球及全局球场交互建模，利用门控池化聚合稀疏时间证据，实现球员级别的事件预测。大量实验表明，PlayNet显著优于代表性视频级和裁剪基线方法，验证了球员中心建模在细粒度体育视频理解中的优势。数据、代码与模型将公开共享。
原文摘要 · Abstract (English)
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.
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