Video surveillance systems are popular and used in public places such as market places, shopping malls, hospitals, banks, streets, education institutions, city administrative offices, and smart cities to enhance the safety of public lives and assets. Most of the time, the timely and accurate detection of video anomalies is the main objective of security applications. The video anomalies such as anomalous activities and anomalous entities are defined as the abnormal or irregular patterns present in the video that do not conform to the normal trained patterns. Anomalous activities such as fighting, riots, traffic rule violations, and stampede as well as anomalous entities such as weapons at the sensitive place and abandoned luggage should be detected automatically in time. However, the detection of video anomalies is challenging due to the ambiguous nature of the anomaly, various environmental conditions, the complex nature of human behaviors, and the lack of proper datasets. There are only a few dedicated surveys related to deep learning-based video anomaly detection as the research domain is in its early stages. However, state of the art lacks a review that provides a comprehensive study covering all the aspects such as definitions, classifications, modelings, performance evaluation methodologies, open and trending research challenges of video anomaly detection. Hence, in this survey, we present a comprehensive study of the deep learning-based methods reported in state of the art to detect the video anomalies. Further, we discuss the comparative analysis of the state of the art methods in terms of datasets, computational infrastructure, and performance metrics for both quantitative and qualitative analyses. Finally, we outline the challenges and promising directions for further research.

视频监视系统在市场，商场，医院，银行，街道，教育机构，城市行政办公室和智慧城市等公共场所中广为使用并用于提高公共生活和资产的安全性。在大多数情况下，及时，准确地检测视频异常是安全应用程序的主要目标。视频异常（例如异常活动和异常实体）定义为视频中存在的不符合正常训练模式的异常或不规则模式。应当及时自动检测战斗，骚乱，违反交通规则和踩踏等异常活动，以及在敏感地方武器和遗弃行李等异常实体。然而，由于异常的模棱两可，各种环境条件，人类行为的复杂性以及缺乏适当的数据集，因此视频异常的检测具有挑战性。由于研究领域尚处于初期阶段，因此仅有少量专门的调查与基于深度学习的视频异常检测有关。然而，现有技术缺乏评论来提供涵盖所有方面的全面研究，例如定义，分类，建模，性能评估方法，视频异常检测的开放性和趋势性研究挑战。因此，在本次调查中，我们对现有技术中检测视频异常的基于深度学习的方法进行了全面研究。此外，我们将讨论关于数据集的最新方法的比较分析，计算基础架构，以及用于定量和定性分析的性能指标。最后，我们概述了进一步研究的挑战和有希望的方向。