Duong Huu-Thanh, Le Viet-Tuan, Hoang Vinh Truong
Faculty of Information Technology, Ho Chi Minh City Open University, 97 Vo Van Tan, District 3, Ho Chi Minh City 700000, Vietnam.
Sensors (Basel). 2023 May 24;23(11):5024. doi: 10.3390/s23115024.
Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.
视频监控中的异常检测是一个高度发达的课题，正吸引着研究界越来越多的关注。对具有自动检测流媒体视频中异常事件能力的智能系统有巨大需求。因此，人们提出了各种各样的方法来构建一个有效的模型以确保公共安全。已经有各种各样关于异常检测的综述，比如网络异常检测、金融欺诈检测、人类行为分析等等。深度学习已成功应用于计算机视觉的许多方面。特别是，生成模型的强劲发展意味着这些是所提出方法中使用的主要技术。本文旨在对视频异常检测领域中基于深度学习的技术进行全面综述。具体而言，基于深度学习的方法已根据其目标和学习指标被分类为不同的方法。此外，还针对基于视觉的领域深入讨论了预处理和特征工程技术。本文还描述了用于训练和检测异常人类行为的基准数据库。最后，讨论了视频监控中的常见挑战，为未来研究提供一些可能的解决方案和方向。
