梳理体育视频事件检测三大任务，厘清差异并评估现有方法与数据集。
Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges
- 区分时序动作定位、动作定位与精确事件定位三类任务及其应用场景。
- 总结主流方法在时间建模、多模态融合与数据效率上的技术路径。
- 指出数据集依赖高质量转播视频、评价指标宽松等实际应用痛点。
视频事件检测已成为现代体育分析的核心，支撑自动化表现评估、内容生成与战术决策。深度学习推动了时序动作定位（TAL）、动作定位（AS）和精确事件定位（PES）等任务的发展，分别用于检测动作段落、代表性时间戳与精确帧级事件。尽管三者密切相关，但其细微差别常被混淆，导致研究与应用中的困惑。以往综述或覆盖通用视频事件检测，或涵盖更广泛的体育视频任务，却忽视了事件定位特有的时间粒度与领域挑战。多数体育视频综述聚焦精英赛事，忽略日常参与者。本文通过：(i) 明确界定 TAL、AS 与 PES 及其使用场景；(ii) 构建包括时间建模策略、多模态框架与数据高效流水线在内的系统分类体系；(iii) 批判性评估基准数据集与评估协议，揭示其对广播质量视频的依赖及过度奖励宽松多标签预测的问题。该工作整合当前研究，揭示开放挑战，为研究与产业界构建时间精准、可泛化且可部署的体育事件检测系统提供全面基础。
原文摘要 · Abstract (English)
Video event detection has become a cornerstone of modern sports analytics, powering automated performance evaluation, content generation, and tactical decision-making. Recent advances in deep learning have driven progress in related tasks such as Temporal Action Localization (TAL), which detects extended action segments; Action Spotting (AS), which identifies a representative timestamp; and Precise Event Spotting (PES), which pinpoints the exact frame of an event. Although closely connected, their subtle differences often blur the boundaries between them, leading to confusion in both research and practical applications. Furthermore, prior surveys either address generic video event detection or broader sports video tasks, but largely overlook the unique temporal granularity and domain-specific challenges of event spotting. In addition, most existing sports video surveys focus on elite-level competitions while neglecting the wider community of everyday practitioners. This survey addresses these gaps by: (i) clearly delineating TAL, AS, and PES and their respective use cases; (ii) introducing a structured taxonomy of state of the art approaches including temporal modeling strategies, multimodal frameworks, and data-efficient pipelines tailored for AS and PES; and (iii) critically assessing benchmark datasets and evaluation protocols, highlighting limitations such as reliance on broadcast quality footage and metrics that over reward permissive multilabel predictions. By synthesizing current research and exposing open challenges, this work provides a comprehensive foundation for developing temporally precise, generalizable, and practically deployable sports event detection systems for both the research and industry communities.
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