OSL-ActionSpotting：一个用于体育视频中动作识别的统一库
OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videos
AI总结：
本文提出OSL-ActionSpotting这一统一Python库，整合三种主流动作识别方法，在保持原有效果的同时提升易用性，推动体育视频分析研究。
摘要：
动作识别在体育分析中至关重要，因为它能够精确识别和分类体育比赛中的关键时刻，为表现分析和战术决策提供必要的洞察。然而，现有方法的碎片化阻碍了体育分析的进展，因此需要一个统一的代码库来支持视频分析中动作识别的开发与部署。在这项工作中，我们介绍了OSL-ActionSpotting，这是一个Python库，它统一了不同的动作识别算法，以简化体育视频分析中的研究和应用。OSL-ActionSpotting将多种最先进的技术封装到一个单一、用户友好的框架中，为跨多个数据集的动作识别和分析提供标准化流程。我们成功地将三种基础性的动作识别方法集成到OSL-ActionSpotting中，所取得的性能指标与原始分散代码库的性能指标相匹配。这种在单一库中的统一既保留了每种方法的有效性，又提升了体育分析领域研究人员和从业者的易用性和可访问性。通过弥合各种动作识别技术之间的差距，OSL-ActionSpotting对体育视频分析领域做出了重要贡献，促进了分析能力的增强和协作研究机会。该库的可扩展和模块化设计确保了其长期相关性以及对未来该领域技术进步的适应性。
英文摘要：
Action spotting is crucial in sports analytics as it enables the precise identification and categorization of pivotal moments in sports matches, providing insights that are essential for performance analysis and tactical decision-making. The fragmentation of existing methodologies, however, impedes the progression of sports analytics, necessitating a unified codebase to support the development and deployment of action spotting for video analysis. In this work, we introduce OSL-ActionSpotting, a Python library that unifies different action spotting algorithms to streamline research and applications in sports video analytics. OSL-ActionSpotting encapsulates various state-of-the-art techniques into a singular, user-friendly framework, offering standardized processes for action spotting and analysis across multiple datasets. We successfully integrated three cornerstone action spotting methods into OSL-ActionSpotting, achieving performance metrics that match those of the original, disparate codebases. This unification within a single library preserves the effectiveness of each method and enhances usability and accessibility for researchers and practitioners in sports analytics. By bridging the gaps between various action spotting techniques, OSL-ActionSpotting significantly contributes to the field of sports video analysis, fostering enhanced analytical capabilities and collaborative research opportunities. The scalable and modularized design of the library ensures its long-term relevance and adaptability to future technological advancements in the domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
