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A comprehensive review of deep learning approaches for group activity analysis
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Journal Article
A comprehensive review of deep learning approaches for group activity analysis
2025
Overview
The study of group activity analysis has garnered significant attention. Group activity offers a unique perspective on the relationships between individuals, providing insights that individual and crowd activities may not reveal. This paper aims to contribute to the existing body of knowledge by providing a comprehensive review of the methods employed in utilizing deep learning for the analysis of group activity. The review encompasses an overview of various methodologies for group detection, segmentation, feature extraction, and description. Additionally, it delves into the classification, recognition, and prediction of group activities, including crowd trajectory prediction. The representation of crowd activity patterns and labels, along with an exploration of datasets for crowd activity analysis, is also included. Ultimately, the paper concludes with a discussion of potential future research directions in the field. By offering a comprehensive review of the advancements in group activity analysis through the lens of deep learning, this paper aims to provide researchers with a better understanding of the field's progress, thereby contributing to the continued development of this area of study.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
