Computer vision and video understanding have transformed sports analytics by enabling large-scale, automated analysis of game dynamics from broadcast footage. Despite significant advances in player and ball tracking, pose estimation, action localization, and automatic foul recognition, anticipating actions before they occur in sports videos has received comparatively little attention. This work introduces the task of action anticipation in basketball broadcast videos, focusing on predicting which team will gain possession of the ball following a shot attempt. To benchmark this task, a new self-curated dataset comprising 100,000 basketball video clips, over 300 hours of footage, and more than 2,000 manually annotated rebound events is presented. Comprehensive baseline results are reported using state-of-the-art action anticipation methods, representing the first application of deep learning techniques to basketball rebound prediction. Additionally, two complementary tasks, rebound classification and rebound spotting, are explored, demonstrating that this dataset supports a wide range of video understanding applications in basketball, for which no comparable datasets currently exist. Experimental results highlight both the feasibility and inherent challenges of anticipating rebounds, providing valuable insights into predictive modeling for dynamic multi-agent sports scenarios. By forecasting team possession before rebounds occur, this work enables applications in real-time automated broadcasting and post-game analysis tools to support decision-making.

翻译：计算机视觉与视频理解技术通过支持对广播录像进行大规模自动化比赛动态分析，已彻底改变了体育分析领域。尽管在球员与球的追踪、姿态估计、动作定位及自动犯规识别方面取得了显著进展，但在体育视频中预测动作发生前的事件却相对较少受到关注。本研究提出了篮球广播视频中的动作预测任务，重点预测投篮尝试后哪支球队将获得球权。为基准测试此任务，我们提出了一个新的自建数据集，包含10万个篮球视频片段、超过300小时的录像以及2000多个手动标注的篮板事件。我们使用最先进的动作预测方法报告了全面的基线结果，这代表了深度学习技术在篮球篮板预测中的首次应用。此外，我们还探索了两个互补任务——篮板分类与篮板识别，证明该数据集支持篮球中广泛的视频理解应用，而目前尚无类似数据集存在。实验结果既凸显了预测篮板的可行性，也揭示了其内在挑战，为动态多智能体体育场景的预测建模提供了宝贵见解。通过预测篮板发生前的球队球权归属，本研究为实时自动化广播和赛后分析工具中的决策支持应用提供了可能。