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Action scene understanding in soccer is a challenging task due to the complexand dynamic nature of the game, as well as the interactions between players.This article provides a comprehensive overview of this task divided into actionrecognition, spotting, and spatio-temporal action localization, with aparticular emphasis on the modalities used and multimodal methods. We explorethe publicly available data sources and metrics used to evaluate models'performance. The article reviews recent state-of-the-art methods that leveragedeep learning techniques and traditional methods. We focus on multimodalmethods, which integrate information from multiple sources, such as video andaudio data, and also those that represent one source in various ways. Theadvantages and limitations of methods are discussed, along with their potentialfor improving the accuracy and robustness of models. Finally, the articlehighlights some of the open research questions and future directions in thefield of soccer action recognition, including the potential for multimodalmethods to advance this field. Overall, this survey provides a valuableresource for researchers interested in the field of action scene understandingin soccer.

Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives