Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review

TL;DR

This review summarizes recent deep learning techniques for crowd behaviour prediction and recognition, highlighting methodological advances, applications, and future research directions in this rapidly evolving field.

Contribution

It provides a comprehensive overview of recent deep learning methods applied to crowd behaviour analysis, including physics-based approaches and detailed comparisons.

Findings

Deep neural networks effectively analyze crowd behaviors.

Physics-informed deep learning enhances prediction accuracy.

The field is rapidly evolving with promising future directions.

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