基于深度学习的人群异常行为检测综述

Deep Learning for Abnormal Crowd Behavior Detection:A Review

随着安防需求的日益增长,人群异常行为检测已经成为计算机视觉的研究热点。人群异常行为检测旨在对监控视频中行人的行为进行建模和分析,区分出人群中的正常行为和异常行为,及时发现灾难和意外事件。文中对基于深度学习的人群异常行为检测算法进行了梳理总结。首先,针对人群异常行为检测任务及其现状进行介绍;其次,重点探讨卷积神经网络、自编码网络和生成对抗网络在人群异常行为检测任务中的研究进展;然后,列举该领域常用的数据集,并比较和分析了深度学习方法在UCSD行人数据集上的性能;最后,总结人群异常行为检测的任务难点,并对该领域的未来发展趋势进行了展望。

With the increasing demand of security industry,abnormal crowd behavior detection has become a hot research issue in computer vision.Abnormal crowd behavior detection aims to model and analyze the behavior of pedestrians in surveillance videos,distinguish between normal and abnormal behaviors in the crowd,and discover disasters and accidents in time.A large number of algorithms for abnormal crowd behavior detection based on deep learning are summarized in this paper.First,abnormal crowd behavior detection task and its current research situation are briefly introduced.Second,the research progress of convolutional neural networks,auto-encoder and generative adversarial networks on abnormal crowd behavior detection are discussed separately.Then,some commonly used datasets are listed,and the performance of deep learning methods on UCSD pedestrian datasets are compared and analyzed.Finally,the development difficulties of abnormal crowd behavior detection tasks are summarized,and its future research directions are discussed.