监控视频的异常检测与建模综述

Anomaly Detection and Modeling of Surveillance Video

随着物联网技术的不断发展,监控设备在交通干道、学校医院、商场超市、小区楼宇等公共区域进行了广泛部署.这些监控设备为人们提供了一种隐性安全保障,也产生了大量的监控视频.基于监控视频的异常检测一直是图像处理、机器视觉、深度学习等相关领域的研究热点.对视频异常进行了直观描述和异常检测概述,对出现的一些综述文章进行了分析,针对其覆盖范围不全和特征表示以及模型没有清晰划分.首先从异常检测特征表示、异常检测建模2方面对传统经典的和新兴的视频异常检测算法进行分类和描述.然后从基于距离、概率、重构3个方面将不同的算法进行比较,分析不同模型的优缺点以及每种模型的特性.并对现存算法的评估标准进行归纳并指出了新的更加准确有效的评估指标.最后,介绍了监控视频异常检测常用的数据集,汇总了不同算法在常用数据集上的检测效果,并对未来的研究在实际应用中面临的一些挑战和研究方向进行了探讨.

With the development of Internet of Things technology,monitoring equipment has been widely deployed in public areas such as traffic arteries,schools and hospitals,shopping malls and supermarkets,and residential buildings.These devices provide a hidden safety and generate a lot of surveillance videos.Anomaly detection based on surveillance videos involves research efforts in image processing,machine vision,deep learning,and other related fields.In the paper,the intuitionistic description and anomaly detection of video anomalies are simply summarized,and some review articles did not cover the complete research scope about feature representation and modeling of the anomaly detection,as well as vague division.The research based on video anomaly detection is comprehensively analyzed.Firstly,the traditional classical and emerging video anomaly detection algorithms are classified and described from the aspects of anomaly detection feature representation and modeling.Then,we compare different algorithms based on distance,probability,and reconstruction,analyze the advantages and disadvantages of different models and characteristics of each model.Furthermore,we conclude the evaluation criteria of existing approaches and give the new accurate efficient evaluation index.Finally,we introduce the common datasets of surveillance videos on anomaly detection,summarize the detection effects of different algorithms on the common datasets,and discuss some challenges and future research directions in practical application.