Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. Timely detection of traffic violations and abnormal behavior of pedestrians at public places through computer vision and visual surveillance can be highly effective for maintaining traffic order in cities. However, despite a handful of computer vision–based techniques proposed in recent times to understand the traffic violations or other types of on-road anomalies, no methodological survey is available that provides a detailed insight into the classification techniques, learning methods, datasets, and application contexts. Thus, this study aims to investigate the recent visual surveillance–related research on anomaly detection in public places, particularly on road. The study analyzes various vision-guided anomaly detection techniques using a generic framework such that the key technical components can be easily understood. Our survey includes definitions of related terminologies and concepts, judicious classifications of the vision-guided anomaly detection approaches, detailed analysis of anomaly detection methods including deep learning–based methods, descriptions of the relevant datasets with environmental conditions, and types of anomalies. The study also reveals vital gaps in the available datasets and anomaly detection capability in various contexts, and thus gives future directions to the computer vision–guided anomaly detection research. As anomaly detection is an important step in automatic road traffic surveillance, this survey can be a useful resource for interested researchers working on solving various issues of Intelligent Transportation Systems (ITS).

中文翻译：

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使用视觉监控的道路交通异常检测

计算机视觉在过去十年中已经发展成为替代人工监督的众多应用的关键技术。通过计算机视觉和视觉监控，及时发现公共场所的交通违法行为和行人异常行为，对维护城市交通秩序非常有效。然而，尽管最近提出了一些基于计算机视觉的技术来了解交通违规或其他类型的道路异常，但没有提供对分类技术、学习方法、数据集和应用程序上下文。因此，本研究旨在调查最近与视觉监控相关的公共场所异常检测研究，特别是在道路上。该研究使用通用框架分析了各种视觉引导的异常检测技术，以便可以轻松理解关键技术组件。我们的调查包括相关术语和概念的定义、视觉引导异常检测方法的明智分类、异常检测方法的详细分析，包括基于深度学习的方法、环境条件相关数据集的描述以及异常类型。该研究还揭示了各种情况下可用数据集和异常检测能力的重要差距，从而为计算机视觉引导的异常检测研究提供了未来方向。由于异常检测是自动道路交通监控的重要步骤，