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

TL;DR

This survey reviews recent advances in soccer action recognition, spotting, and localization, emphasizing multimodal methods, datasets, and future research directions to improve model accuracy and robustness.

Contribution

It provides a comprehensive overview of current methods, datasets, and challenges in soccer action scene understanding, highlighting multimodal approaches and open research questions.

Findings

Deep learning methods dominate recent approaches

Multimodal data improves recognition accuracy

Open challenges include data scarcity and real-time processing

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.