Abstract
Video event detection has become an essential component of sports analytics, enabling automated identification of key moments and enhancing performance analysis, viewer engagement, and broadcast efficiency. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs) and Transformers, have significantly improved accuracy and efficiency in Temporal Action Localization (TAL), Action Spotting (AS), and Precise Event Spotting (PES). This survey provides a comprehensive overview of these three key tasks, emphasizing their differences, applications, and the evolution of methodological approaches. We thoroughly review and categorize existing datasets and evaluation metrics specifically tailored for sports contexts, highlighting the strengths and limitations of each. Furthermore, we analyze state-of-the-art techniques, including multi-modal approaches that integrate audio and visual information, methods utilizing self-supervised learning and knowledge distillation, and approaches aimed at generalizing across multiple sports. Finally, we discuss critical open challenges and outline promising research directions toward developing more generalized, efficient, and robust event detection frameworks applicable to diverse sports. This survey serves as a foundation for future research on efficient, generalizable, and multi-modal sports event detection.
Abstract (translated)
视频事件检测已经成为体育分析的一个关键组成部分，它能够自动识别关键时刻，并增强性能分析、观众参与度和广播效率。近年来，深度学习的进展，特别是卷积神经网络（CNNs）和Transformer模型，在时间动作定位（TAL）、动作检测（AS）以及精确事件检测（PES）方面显著提高了准确性和效率。本文综述详细概述了这三个关键任务，并强调它们之间的差异、应用及方法论演进。我们全面回顾并分类现有的专门针对体育背景的数据集和评估指标，突显每个数据集和评价标准的优势与局限性。此外，我们分析了最新的技术趋势，包括多模态方法（融合音频和视觉信息）、利用自监督学习和知识蒸馏的方法，以及跨多个运动泛化的策略。最后，本文讨论了一些关键的开放挑战，并概述了有前景的研究方向，旨在开发适用于各种体育项目的更通用、高效且鲁棒的事件检测框架。这项综述为未来关于高效的、可扩展性和多模态体育事件检测研究奠定了基础。
URL
https://arxiv.org/abs/2505.03991
