Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts.

翻译：围绕公共安全的日益担忧已引起全球众多政府和安全机构的关注。这些实体逐渐认识到，迫切需要可靠且安全的群体监控系统来应对这些问题。有效管理人群聚集需要采取主动措施，以防止突发事件或复杂情况，确保安全且协调有序的环境。针对群体监控系统及其安全影响的研究匮乏，催生了一个新兴的研究领域，探索有效保护人群聚集的潜在方法。群体监控系统依赖于一种分叉方法，涵盖基于视觉和非基于视觉的技术。本研究将对这两种方法进行深入分析。这些方法的有效性取决于部署的具体环境和时间背景，因为它们各自具有独特优势。本文旨在深入分析近期人工智能（AI）算法和模型在自动化系统中的整合，重点探讨其在不同环境中的当代应用及有效性。