智能视频异常事件检测方法综述

A survey of video abnormal event detection

视频异常事件检测问题是计算机视觉领域的重要研究课题之一,旨在基于模式识别和计算机视觉方法智能地从监控视频中自动检测出需要关注的异常事件或行为,在实际生活中有广泛的应用和巨大的潜在需求,是人工智能技术落地的重要方向之一。同时,近年来以深度学习为代表的新兴机器学习技术及其在各个领域中取得的巨大成功,极大地启发了各类先进技术在视频异常事件检测问题中的应用。首先回顾了视频异常事件检测问题的定义和面临的主要挑战,随后从视频异常检测包含的3个最主要的技术环节(视频事件提取、视频事件表示、视频事件建模与检测)对当前主流视频异常事件检测技术进行了介绍,并对其各自的优缺点进行了分析和总结。最后,介绍视频异常检测领域中常用的基准测试数据集和相应的评价指标,对比当前主流方法的视频异常事件检测性能,对这些方法进行讨论并给出结论和展望。

Video anomaly detection is one of the most significant research tasks in computer vision area.It aims to intelligently identify the events that do not conform to expected behavior based on pattern recognition and computer vision methods.Video anomaly detection is widely applied and there is an enormous potential demand in modern society.Meanwhile,inspired by the successful achievements in various area of emerging deep learning technologies,more and more newly-emerged methods are conducted on video anomaly detection problem.Firstly,we retrospect the definition and main challenges of vi-deo anomaly detection.Secondly,we introduce the mainstream video anomaly detection methods from three primary technical steps(video event extraction,video event representation,video event modeling and detection)of video anomaly detection,and conclude their advantages as well as drawbacks respectively.Finally,we introduce the benchmark datasets and evaluation metrics of video anomaly detection,compare the performance of mainstream methods and give conclusions and prospects.