论文标题
视频异常检测的深度学习技术的调查
A Survey on Deep Learning Techniques for Video Anomaly Detection
论文作者
论文摘要
视频中的异常检测是一个已经研究了十多年的问题。由于其广泛的适用性，该领域激起了研究人员的兴趣。因此，多年来已经提出了各种各样的方法，这些方法从基于统计的方法到基于机器学习的方法。已经在该领域进行了许多调查，但本文着重于使用深度学习的近期检测领域的最新进展提供概述。深度学习已成功地应用于许多人工智能领域，例如计算机视觉，自然语言处理等。但是，这项调查的重点是深度学习如何改善，并为视频异常检测的领域提供了更多见解。本文提供了有关其目标的不同深度学习方法的分类。此外，它还讨论了常用的数据集以及常见的评估指标。之后，进行了讨论，综合了所有最近的方法，以提供指导和可能的未来研究领域。
Anomaly detection in videos is a problem that has been studied for more than a decade. This area has piqued the interest of researchers due to its wide applicability. Because of this, there has been a wide array of approaches that have been proposed throughout the years and these approaches range from statistical-based approaches to machine learning-based approaches. Numerous surveys have already been conducted on this area but this paper focuses on providing an overview on the recent advances in the field of anomaly detection using Deep Learning. Deep Learning has been applied successfully in many fields of artificial intelligence such as computer vision, natural language processing and more. This survey, however, focuses on how Deep Learning has improved and provided more insights to the area of video anomaly detection. This paper provides a categorization of the different Deep Learning approaches with respect to their objectives. Additionally, it also discusses the commonly used datasets along with the common evaluation metrics. Afterwards, a discussion synthesizing all of the recent approaches is made to provide direction and possible areas for future research.
