  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.
