Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 507-513.DOI: 10.11772/j.issn.1001-9081.2021122081
• Multimedia computing and computer simulation • Previous Articles
Qing JIA(), Laihua WANG, Weisheng WANG
Received:
2021-12-09
Revised:
2022-04-13
Accepted:
2022-05-13
Online:
2022-06-13
Published:
2023-02-10
Contact:
Qing JIA
About author:
WANG Laihua, born in 1988, Ph. D., associate professor. Her research interests include digital image processing, video anomaly detection.Supported by:
通讯作者:
贾晴
作者简介:
王来花(1988—),女,山东聊城人,副教授,博士,主要研究方向:数字图像处理、视频异常检测基金资助:
CLC Number:
Qing JIA, Laihua WANG, Weisheng WANG. Anomaly detection in video via independently recurrent neural network and variational autoencoder network[J]. Journal of Computer Applications, 2023, 43(2): 507-513.
贾晴, 王来花, 王伟胜. 基于独立循环神经网络与变分自编码网络的视频帧异常检测[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 507-513.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021122081
方法 | 类型 | Ped1 | Ped2 | Avenue | |||
---|---|---|---|---|---|---|---|
AUC | EER | AUC | EER | AUC | EER | ||
Conv-AE[ | 帧重构 | 75.0 | 27.9 | 85.0 | 21.7 | 80.0 | 23.0 |
Unmask[ | 帧重构 | 68.4 | — | 82.2 | — | 80.6 | — |
FP [ | 帧预测 | 83.1 | — | 95.4 | — | 84.9 | — |
AD[ | 帧预测 | 83.9 | — | 96.0 | — | 86.0 | — |
GMFC-VAE[ | 帧重构 | 94.9 | 11.3 | 92.2 | 12.6 | 83.4 | 22.7 |
R-STAE[ | 帧重构 | — | — | 83.0 | — | 82.0 | — |
R-VAE[ | 帧重构 | 75.0 | 32.4 | 91.0 | 15.5 | 79.6 | 27.5 |
本文方法 | 帧预测 | 84.3 | 22.7 | 96.2 | 8.8 | 86.6 | 19.0 |
Tab. 1 AUC value and EER value comparison of related abnormal detection methods
方法 | 类型 | Ped1 | Ped2 | Avenue | |||
---|---|---|---|---|---|---|---|
AUC | EER | AUC | EER | AUC | EER | ||
Conv-AE[ | 帧重构 | 75.0 | 27.9 | 85.0 | 21.7 | 80.0 | 23.0 |
Unmask[ | 帧重构 | 68.4 | — | 82.2 | — | 80.6 | — |
FP [ | 帧预测 | 83.1 | — | 95.4 | — | 84.9 | — |
AD[ | 帧预测 | 83.9 | — | 96.0 | — | 86.0 | — |
GMFC-VAE[ | 帧重构 | 94.9 | 11.3 | 92.2 | 12.6 | 83.4 | 22.7 |
R-STAE[ | 帧重构 | — | — | 83.0 | — | 82.0 | — |
R-VAE[ | 帧重构 | 75.0 | 32.4 | 91.0 | 15.5 | 79.6 | 27.5 |
本文方法 | 帧预测 | 84.3 | 22.7 | 96.2 | 8.8 | 86.6 | 19.0 |
方法 | FPS | 方法 | FPS |
---|---|---|---|
Unmask[ | 20 | R-STAE[ | 14 |
FP[ | 25 | 本文方法 | 28 |
Tab. 2 Time performance comparison of related abnormal detection methods
方法 | FPS | 方法 | FPS |
---|---|---|---|
Unmask[ | 20 | R-STAE[ | 14 |
FP[ | 25 | 本文方法 | 28 |
方法 | Ped1 | Ped2 | Avenue |
---|---|---|---|
Conv-AE[ | 0.243 | 0.384 | 0.256 |
FP [ | 0.259 | 0.469 | 0.275 |
本文方法 | 0.263 | 0.497 | 0.293 |
Tab. 3 Difference value ΔS comparison on different datasets
方法 | Ped1 | Ped2 | Avenue |
---|---|---|---|
Conv-AE[ | 0.243 | 0.384 | 0.256 |
FP [ | 0.259 | 0.469 | 0.275 |
本文方法 | 0.263 | 0.497 | 0.293 |
方法 | AUC | EER |
---|---|---|
Base | 94.0 | 12.4 |
Base+IndRNN | 95.6 | 10.9 |
Base+IndRNN+GAN | 96.2 | 8.8 |
Tab. 4 Performance of different module combinations in network
方法 | AUC | EER |
---|---|---|
Base | 94.0 | 12.4 |
Base+IndRNN | 95.6 | 10.9 |
Base+IndRNN+GAN | 96.2 | 8.8 |
损失函数 | AUC |
---|---|
梯度损失+多尺度结构相似性损失 | 93.9 |
梯度损失+混合损失 | 95.9 |
梯度损失+混合损失+全变分损失 | 96.2 |
Tab. 5 Performance of different loss functions combinations in network
损失函数 | AUC |
---|---|
梯度损失+多尺度结构相似性损失 | 93.9 |
梯度损失+混合损失 | 95.9 |
梯度损失+混合损失+全变分损失 | 96.2 |
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