监控视频中异常事件检测技术研究进展

Progress on Abnormal Event Detection Technology in Video Surveillance

异常事件检测技术是当前智能监控技术研究领域关注的一个热点,作为计算机视觉的重要研究内容,其主要目标是利用计算机自动检测出可被视为异常的事件。传统方法存在低层视频特征描述能力弱,异常检测方法计算代价大,对复杂场景建模时鲁棒性差等方面的限制。本文结合国内外的研究现状和目前的主流方法,介绍了监控视频中异常事件检测涉及的基本技术,分析了各类监控视频特征提取方法、特征学习模型和异常检测方法的优缺点,整理归纳了可用于监控视频中异常事件检测的常用实验数据集,最后讨论了监控视频中异常事件检测技术的难点、挑战及未来发展趋势。

Abnormal event detection is a hot topic in the field of intelligent surveillance monitoring technology research currently.As an important research content of computer vision,its main goal is to use computers to automatically detect abnormal events.Traditional methods have limitations in the weakness of low-level video feature description ability,high computational cost of anomaly detection methods and poor robustness in modeling complex scenes.In recent years,how to design a high-level semantic feature extraction method,accelerate the process of abnormal event detection,and model complex scenes such as multiple cameras have become the forefront topic of current research.Based on the research situation at home and abroad and the mainstream methods,this paper introduces the basic techniques involved in abnormal event detection in surveillance videos,and analyzes the advantages and disadvantages of various types of surveillance video feature extraction methods,feature learning models and anomaly detection methods.This paper also summarizes the commonly used benchmark datasets that can be used to abnormal event detection in surveillance videos.Finally,we discuss the difficulties,challenges and future development trends of abnormal event detection in surveillance videos.