Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia.
Sensors (Basel). 2022 Aug 14;22(16):6080. doi: 10.3390/s22166080.
With the widespread use of closed-circuit television (CCTV) surveillance systems in public areas, crowd anomaly detection has become an increasingly critical aspect of the intelligent video surveillance system. It requires workforce and continuous attention to decide on the captured event, which is hard to perform by individuals. The available literature on human action detection includes various approaches to detect abnormal crowd behavior, which is articulated as an outlier detection problem. This paper presents a detailed review of the recent development of anomaly detection methods from the perspectives of computer vision on different available datasets. A new taxonomic organization of existing works in crowd analysis and anomaly detection has been introduced. A summarization of existing reviews and datasets related to anomaly detection has been listed. It covers an overview of different crowd concepts, including mass gathering events analysis and challenges, types of anomalies, and surveillance systems. Additionally, research trends and future work prospects have been analyzed.
随着闭路电视（CCTV）监控系统在公共场所的广泛应用，人群异常检测已成为智能视频监控系统中越来越重要的一个方面。它需要人力和持续关注来判断所捕获的事件，而这很难由个人来完成。现有的关于人体动作检测的文献包括各种方法来检测异常的人群行为，这可以表述为异常检测问题。本文从计算机视觉的角度，在不同的可用数据集上，对异常检测方法的最新发展进行了详细的回顾。引入了一种新的人群分析和异常检测现有工作的分类组织。列出了与异常检测相关的现有综述和数据集的摘要。它涵盖了不同人群概念的概述，包括大规模集会事件的分析和挑战、异常的类型以及监控系统。此外，还分析了研究趋势和未来工作前景。
