综述深度学习在人群行为分析中的最新进展，助力安全与城市规划。
Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review
- 梳理深度神经网络在人群行为预测与识别中的应用方法。
- 对比物理结合深度学习的新模型，评估其在真实场景中的表现。
- 适合刚入行的研究者快速掌握领域脉络与未来方向。
人群行为分析对公共安全、城市规划等实际应用至关重要，已有数十年研究历史。近十年来，深度学习的发展显著推动了该领域的研究进展。本文综述了基于深度学习的人群行为分析最新成果，重点聚焦两大核心任务：人群行为预测与识别。涵盖从机器学习中提出的各类深度神经网络模型，包括纯深度网络以及近期融合物理规律的方法。同时，对代表性研究进行详细讨论与比较。最后，评估现有方法的有效性，并探讨该快速演进领域未来的研究方向。本章旨在为新进入该领域的研究者提供高层次的综述，帮助其快速把握研究现状，也为资深研究者提供回顾与展望的参考。
原文摘要 · Abstract (English)
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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