Abstract
Action scene understanding in soccer is a challenging task due to the complex and dynamic nature of the game, as well as the interactions between players. This article provides a comprehensive overview of this task divided into action recognition, spotting, and spatio-temporal action localization, with a particular emphasis on the modalities used and multimodal methods. We explore the publicly available data sources and metrics used to evaluate models' performance. The article reviews recent state-of-the-art methods that leverage deep learning techniques and traditional methods. We focus on multimodal methods, which integrate information from multiple sources, such as video and audio data, and also those that represent one source in various ways. The advantages and limitations of methods are discussed, along with their potential for improving the accuracy and robustness of models. Finally, the article highlights some of the open research questions and future directions in the field of soccer action recognition, including the potential for multimodal methods to advance this field. Overall, this survey provides a valuable resource for researchers interested in the field of action scene understanding in soccer.
Abstract (translated)
在足球比赛中的动作场景理解是一个具有挑战性的任务,因为足球比赛具有复杂和动态的特点,以及球员之间的互动。本文对这个任务进行了全面综述,并将其分成动作识别、发现和时间和空间动作定位,其中特别注重使用的模式和多种模式方法。我们探索了可用于评估模型性能的公开可用数据源和指标。本文回顾了最近利用深度学习技术和传统方法的最新方法。我们重点探讨了多种模式方法,它们整合了来自多个来源的信息,例如视频和音频数据,以及以不同方式代表一个来源的方法。方法的优点和局限性被讨论,以及它们如何提高模型的准确性和鲁棒性的潜力。最后,文章强调了足球动作识别领域的一些开放研究问题和未来的研究方向,包括多种模式方法推动该领域的进步的潜力。总的来说,本文为对足球动作场景理解领域感兴趣的研究人员提供了宝贵的资源。
URL
https://arxiv.org/abs/2309.12067
