Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review

• URL: http://arxiv.org/abs/2505.18401v1
• Date: Fri, 23 May 2025 22:08:35 GMT
• Title: Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review
• Authors: Jiangbei Yue, He Wang,
• Abstract summary: Crowd behaviour analysis is essential to numerous real-world applications, such as public safety and urban planning.<n>The development of deep learning has significantly propelled the research on crowd behaviours.<n>This chapter aims to provide a high-level summary of the ongoing deep learning research in crowd behaviour analysis.
• Score: 9.531376461775462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crowd behaviour analysis is essential to numerous real-world applications, such as public safety and urban planning, and therefore has been studied for decades. In the last decade or so, the development of deep learning has significantly propelled the research on crowd behaviours. This chapter reviews recent advances in crowd behaviour analysis using deep learning. We mainly review the research in two core tasks in this field, crowd behaviour prediction and recognition. We broadly cover how different deep neural networks, after first being proposed in machine learning, are applied to analysing crowd behaviours. This includes pure deep neural network models as well as recent development of methodologies combining physics with deep learning. In addition, representative studies are discussed and compared in detail. Finally, we discuss the effectiveness of existing methods and future research directions in this rapidly evolving field. This chapter aims to provide a high-level summary of the ongoing deep learning research in crowd behaviour analysis. It intends to help new researchers who just entered this field to obtain an overall understanding of the ongoing research, as well as to provide a retrospective analysis for existing researchers to identify possible future directions

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