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

Analyzing action scenes 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 key moments, and identifying actions in both time and space (spatio-temporal action localization) in soccer. We explore 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 approaches. Our analysis begins with methods based on feature engineering, followed by an exploration of various deep learning techniques. This includes using Convolutional Neural Networks (CNNs) for visual information processing, Recurrent Neural Networks (RNNs) for analyzing temporal sequences, and transformer architectures to effectively capture context. In particular, we focus on the specifics of multimodal data, illustrating the potential for improved model accuracy and robustness. This includes an exploration of methods that integrate information from multiple sources, such as video and audio data, and methods that represent a single data source through multiple analytical lenses, offering a richer, more nuanced understanding of soccer actions (e.g., using a graph representation of players). Finally, the article highlights some of the open research questions and future directions in the field of soccer action analysis, especially the potential for multimodal methods to advance this field. Overall, this survey provides a valuable resource for researchers interested in the field of analyzing action scenes in soccer.