Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep-learning approaches have made it possible to develop fully-automated vision-based crowd-monitoring applications. However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome. In this article, we delve into six major areas of visual crowd analysis, emphasizing the key developments in each of these areas. We outline the crucial unresolved issues that must be tackled in future works, in order to ensure that the field of automated crowd monitoring continues to progress and thrive. Several surveys related to this topic have been conducted in the past. Nonetheless, this article thoroughly examines and presents a more intuitive categorization of works, while also depicting the latest breakthroughs within the field, incorporating more recent studies carried out within the last few years in a concise manner. By carefully choosing prominent works with significant contributions in terms of novelty or performance gains, this paper presents a more comprehensive exposition of advancements in the current state-of-the-art.

翻译：在过去十年中，计算机视觉领域对自动人群监控的兴趣显著增长。现代深度学习方法使得开发基于视觉的全自动人群监控应用成为可能。然而，尽管问题本身规模庞大、技术进步显著且研究界持续关注，仍有许多挑战有待克服。本文深入探讨了视觉人群分析的六大主要领域，强调每个领域的关键进展，并阐述了未来工作中必须解决的关键未决问题，以确保自动人群监控领域持续进步与繁荣。虽然过去已有多篇相关综述，但本文对相关工作进行了更直观的分类梳理，同时以简洁方式呈现了该领域近几年的最新突破。通过精心挑选在创新性或性能提升方面具有显著贡献的代表性工作，本文对当前最先进技术进展提供了更全面的阐述。