计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (3): 529-540.DOI: 10.3778/j.issn.1673-9418.2106117
邬开俊1, 黄涛1,+(), 王迪聪1,2, 白晨帅1, 陶小苗1
收稿日期:
2021-06-21
修回日期:
2021-08-18
出版日期:
2022-03-01
发布日期:
2021-08-19
通讯作者:
+ E-mail: 1479987020@qq.com作者简介:
邬开俊(1978—),男,山东莒南人,博士,教授,博士生导师,主要研究方向为视频检测与神经元的非线性动力学。基金资助:
WU Kaijun1, HUANG Tao1,+(), WANG Dicong1,2, BAI Chenshuai1, TAO Xiaomiao1
Received:
2021-06-21
Revised:
2021-08-18
Online:
2022-03-01
Published:
2021-08-19
About author:
WU Kaijun, born in 1978, Ph.D., professor, Ph.D. supervisor. His research interests include video detection and nonlinear dynamics of neurons.Supported by:
摘要:
视频异常检测是指对偏离正常行为事件的检测识别,在监控视频中有着广泛的应用。对基于深度学习的视频异常检测算法进行了深入的调查研究和全面的梳理与总结。首先,对视频异常检测相关内容以及异常检测面临的挑战进行了分析;然后,从有监督、半监督和无监督三方面对视频异常检测的相关算法进行了介绍和分析。对三种不同场景下的算法进一步细化分类,将监督场景下的算法划分为二分类和多分类两种方式,将半监督场景下的算法划分为计算异常得分和聚类判别两种方式,将无监督场景下的算法划分为重构判别和预测判别两种方式,并且分析了不同技术的特点和优缺点。介绍了目前在视频异常检测领域常用的数据集,以及检测性能的评估标准,对目前主流的视频异常检测算法性能进行了对比分析。最后,对视频异常检测算法的未来研究方向进行了讨论和展望。
中图分类号:
邬开俊, 黄涛, 王迪聪, 白晨帅, 陶小苗. 视频异常检测技术研究进展[J]. 计算机科学与探索, 2022, 16(3): 529-540.
WU Kaijun, HUANG Tao, WANG Dicong, BAI Chenshuai, TAO Xiaomiao. Research Progress of Video Anomaly Detection Technology[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(3): 529-540.
数据集 | 帧数 | 标签 | 分辨率 | 异常事件 |
---|---|---|---|---|
UCSD Ped1 | 14 000 | 时间和空间 | 238×158 | 汽车、骑自行车等 |
UCSD Ped2 | 4 560 | 时间和空间 | 360×240 | 汽车、骑自行车等 |
UMN Lawn | 1 450 | 时间 | 320×240 | 人群逃生 |
UMN Indoor | 4 415 | 时间 | 320×240 | 人群逃生 |
UMN Plaza | 2 145 | 时间 | 320×240 | 人群逃生 |
CUHK Avenue | 30 652 | 时间和空间 | 640×360 | 跑、扔纸、扔书包等 |
Subway Entrance | 72 401 | 时间 | 512×384 | 逃票、徘徊 |
Subway Exit | 136 524 | 时间 | 512×384 | 逃票、徘徊 |
Shanghai-Tech | 317 398 | 时间和空间 | 856×480 | 追逐、突然移动等 |
UCF-Crime | 大约13.8×106 | 视频级和时间 | 320×240 | 逮捕、纵火、攻击等 |
表1 异常检测数据集对比
Table 1 Comparison of anomaly detection datasets
数据集 | 帧数 | 标签 | 分辨率 | 异常事件 |
---|---|---|---|---|
UCSD Ped1 | 14 000 | 时间和空间 | 238×158 | 汽车、骑自行车等 |
UCSD Ped2 | 4 560 | 时间和空间 | 360×240 | 汽车、骑自行车等 |
UMN Lawn | 1 450 | 时间 | 320×240 | 人群逃生 |
UMN Indoor | 4 415 | 时间 | 320×240 | 人群逃生 |
UMN Plaza | 2 145 | 时间 | 320×240 | 人群逃生 |
CUHK Avenue | 30 652 | 时间和空间 | 640×360 | 跑、扔纸、扔书包等 |
Subway Entrance | 72 401 | 时间 | 512×384 | 逃票、徘徊 |
Subway Exit | 136 524 | 时间 | 512×384 | 逃票、徘徊 |
Shanghai-Tech | 317 398 | 时间和空间 | 856×480 | 追逐、突然移动等 |
UCF-Crime | 大约13.8×106 | 视频级和时间 | 320×240 | 逮捕、纵火、攻击等 |
模型 | 监督方式 | 数据集 | 是否端到端 | 方法描述 | AUC/% | |||
---|---|---|---|---|---|---|---|---|
C | U1 | U2 | S | |||||
DSTCNN[ | 监督 | UCSD | 否 | 深度时空卷积神经网络(DSTCNN)提取动作特征并输出正、异常分类概率 | — | 99.7 | 99.9 | — |
LDA-Net[ | 监督 | UCSD | 否 | YOLO提取的前景人体作为 3D CNN的输入提取行为的时空特征进而分类正、异常行为 | — | — | 97.9 | — |
IBL[ | 半监督 | Shanghai-Tech | 否 | 多实例学习 (MIL)定义一种IBL损失来约束弱监督问题的函数空间 | — | — | — | 82.5 |
GCN-Anomaly[ | 半监督 | UCSD+Shanghai-Tech | 是 | 图卷积网络校正噪声标签 | — | — | 93.2 | 84.4 |
AR-Net[ | 半监督 | Shanghai-Tech | 否 | 异常回归网络(AR-Net)的框架学习视频级弱监督下的区分特征 | — | — | — | 91.2 |
App+motion cues[ | 半监督 | UCSD | 否 | 使用“cut-bin”区分异常运动,使用SVDD外观检测 | — | 85.0 | 90.0 | — |
Conv-AE[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 全卷积自编码器学习运动特征 | 70.2 | 81.0 | 90.0 | 60.9 |
ConvLSTM-AE[ | 无监督 | CUHK Avenue | 是 | ConvLSTM-AE框架来检测外观和外观(运动)的变化 | 77.0 | — | — | — |
Sparse coding[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 将稀疏编码和循环神经网络结合 | 81.7 | — | 92.2 | 68.0 |
Future frame[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 采用U-Net来预测下一帧 | 85.1 | 83.1 | 95.4 | 72.8 |
Memory-augmented AE[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 内存增强自编码器(MemAE)查询检索与重构最相关的内存项 | 83.3 | — | 94.1 | 71.2 |
MNAD[ | 无监督 | CUHK Avenue+Shanghai-Tech | 是 | 存储模块记录正常数据的模式 | 88.5 | — | — | 70.5 |
MLEP[ | 无监督 | CUHK Avenue+Shanghai-Tech | 是 | ConvLSTM编码时空信息 | 92.8 | — | — | 76.8 |
Unmasking[ | 无监督 | CUHK Avenue | 否 | 对滑窗内部的前后两部分进行人为分类 | 80.6 | — | — | — |
Appearance-cGAN[ | 无监督 | CUHK Avenue+UCSD | 是 | 两个子网络Conv-AE解决图像结构问题,U-Net解决运动模式的问题 | 86.9 | — | 96.2 | — |
Online-GNG[ | 无监督 | UCSD | 是 | GNG 将不同样本映射到不同聚类实现端到端的聚类 | — | 93.8 | 94.0 | — |
Prediction reconstruction[ | 无监督 | CUHK Avenue+UCSD | 是 | 两个U-Net:一个以帧预测的形式工作,另一个尝试重建前一个网络生成的帧 | 83.7 | 82.6 | 96.2 | — |
Compact features[ | 无监督 | UCSD | 否 | 输入帧分成可变大小的单元结构,提取多重特征并输入多个判别模型进行检测 | — | 82.0 | 84.0 | — |
Plug and play CNN[ | 无监督 | UCSD | 是 | FCN网络作为预训练模型 | — | 95.7 | 88.4 | — |
Siamese distance learning[ | 无监督 | CUHK Avenue+UCSD | 否 | 孪生卷积神经网络学习一对视频时空区域之间的距离函数 | 87.2 | 86.0 | 94.0 | — |
表2 算法效果对比
Table 2 Comparison of algorithm effect
模型 | 监督方式 | 数据集 | 是否端到端 | 方法描述 | AUC/% | |||
---|---|---|---|---|---|---|---|---|
C | U1 | U2 | S | |||||
DSTCNN[ | 监督 | UCSD | 否 | 深度时空卷积神经网络(DSTCNN)提取动作特征并输出正、异常分类概率 | — | 99.7 | 99.9 | — |
LDA-Net[ | 监督 | UCSD | 否 | YOLO提取的前景人体作为 3D CNN的输入提取行为的时空特征进而分类正、异常行为 | — | — | 97.9 | — |
IBL[ | 半监督 | Shanghai-Tech | 否 | 多实例学习 (MIL)定义一种IBL损失来约束弱监督问题的函数空间 | — | — | — | 82.5 |
GCN-Anomaly[ | 半监督 | UCSD+Shanghai-Tech | 是 | 图卷积网络校正噪声标签 | — | — | 93.2 | 84.4 |
AR-Net[ | 半监督 | Shanghai-Tech | 否 | 异常回归网络(AR-Net)的框架学习视频级弱监督下的区分特征 | — | — | — | 91.2 |
App+motion cues[ | 半监督 | UCSD | 否 | 使用“cut-bin”区分异常运动,使用SVDD外观检测 | — | 85.0 | 90.0 | — |
Conv-AE[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 全卷积自编码器学习运动特征 | 70.2 | 81.0 | 90.0 | 60.9 |
ConvLSTM-AE[ | 无监督 | CUHK Avenue | 是 | ConvLSTM-AE框架来检测外观和外观(运动)的变化 | 77.0 | — | — | — |
Sparse coding[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 将稀疏编码和循环神经网络结合 | 81.7 | — | 92.2 | 68.0 |
Future frame[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 采用U-Net来预测下一帧 | 85.1 | 83.1 | 95.4 | 72.8 |
Memory-augmented AE[ | 无监督 | CUHK Avenue+UCSD+Shanghai-Tech | 是 | 内存增强自编码器(MemAE)查询检索与重构最相关的内存项 | 83.3 | — | 94.1 | 71.2 |
MNAD[ | 无监督 | CUHK Avenue+Shanghai-Tech | 是 | 存储模块记录正常数据的模式 | 88.5 | — | — | 70.5 |
MLEP[ | 无监督 | CUHK Avenue+Shanghai-Tech | 是 | ConvLSTM编码时空信息 | 92.8 | — | — | 76.8 |
Unmasking[ | 无监督 | CUHK Avenue | 否 | 对滑窗内部的前后两部分进行人为分类 | 80.6 | — | — | — |
Appearance-cGAN[ | 无监督 | CUHK Avenue+UCSD | 是 | 两个子网络Conv-AE解决图像结构问题,U-Net解决运动模式的问题 | 86.9 | — | 96.2 | — |
Online-GNG[ | 无监督 | UCSD | 是 | GNG 将不同样本映射到不同聚类实现端到端的聚类 | — | 93.8 | 94.0 | — |
Prediction reconstruction[ | 无监督 | CUHK Avenue+UCSD | 是 | 两个U-Net:一个以帧预测的形式工作,另一个尝试重建前一个网络生成的帧 | 83.7 | 82.6 | 96.2 | — |
Compact features[ | 无监督 | UCSD | 否 | 输入帧分成可变大小的单元结构,提取多重特征并输入多个判别模型进行检测 | — | 82.0 | 84.0 | — |
Plug and play CNN[ | 无监督 | UCSD | 是 | FCN网络作为预训练模型 | — | 95.7 | 88.4 | — |
Siamese distance learning[ | 无监督 | CUHK Avenue+UCSD | 否 | 孪生卷积神经网络学习一对视频时空区域之间的距离函数 | 87.2 | 86.0 | 94.0 | — |
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