Abstract
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.
Abstract (translated)
计算机视觉和视频理解技术通过从广播画面中进行大规模自动化分析，已经彻底改变了体育数据分析。尽管在球员追踪、姿态估计、动作定位以及自动犯规识别方面取得了重大进展，但在体育视频中预测即将发生的动作却受到了相对较少的关注。这项工作引入了篮球比赛广播视频中的动作预判任务，重点关注在投篮尝试后哪一队将获得球权的预测。为了评估这一任务，我们提出了一套新的自采集数据集，包括10万段篮球视频片段、超过300小时的影像资料以及2000多个手动标注的篮板事件。 研究报告了使用最先进的动作预判方法所得到的全面基线结果，这是首次将深度学习技术应用于预测篮球比赛中的篮板情况。此外，还探讨了两个互补的任务：篮板分类和篮板定位，这表明该数据集支持广泛的篮球视频理解应用，并且目前没有类似的现有数据集可以比较。 实验结果显示出了在体育比赛中预判篮板的可行性和固有挑战性，为多代理动态场景下的预测建模提供了宝贵的见解。通过提前预测在篮板发生前哪一队将获得球权，这项工作为实时自动化广播和赛后分析工具中的决策支持应用开辟了道路。
URL
https://arxiv.org/abs/2512.15386
