Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (8): 2551-2557.DOI: 10.11772/j.issn.1001-9081.2023081157
• Multimedia computing and computer simulation • Previous Articles Next Articles
Yuhan LIU, Genlin JI(), Hongping ZHANG
Received:
2023-08-29
Revised:
2023-09-20
Accepted:
2023-10-08
Online:
2024-08-22
Published:
2024-08-10
Contact:
Genlin JI
About author:
LIU Yuhan, born in 1999, M. S. candidate. Her research interests include big data analysis and mining, video anomaly detection.Supported by:
通讯作者:
吉根林
作者简介:
刘禹含(1999—),女(满族),吉林吉林人,硕士研究生,CCF会员,主要研究方向:大数据分析与挖掘、视频异常检测基金资助:
CLC Number:
Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention[J]. Journal of Computer Applications, 2024, 44(8): 2551-2557.
刘禹含, 吉根林, 张红苹. 基于骨架图与混合注意力的视频行人异常检测方法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2551-2557.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023081157
特征类别 | 方法 | AUC | |
---|---|---|---|
ShanghaiTech | HR-ShanghaiTech | ||
外观特征 | Mem-AE[ | 0.712 | — |
Multispace[ | 0.736 | — | |
MESDnet[ | 0.732 | — | |
AMAE[ | 0.736 | — | |
STCEN[ | 0.738 | — | |
骨架特征 | MPED-RNN[ | 0.734 | 0.754 |
GEPC[ | 0.736 | 0.755 | |
Normal Graph[ | 0.741 | 0.765 | |
PAD-SGMA | 0.754 | 0.766 |
Tab. 1 Comparison of AUC performance between proposed method and other methods
特征类别 | 方法 | AUC | |
---|---|---|---|
ShanghaiTech | HR-ShanghaiTech | ||
外观特征 | Mem-AE[ | 0.712 | — |
Multispace[ | 0.736 | — | |
MESDnet[ | 0.732 | — | |
AMAE[ | 0.736 | — | |
STCEN[ | 0.738 | — | |
骨架特征 | MPED-RNN[ | 0.734 | 0.754 |
GEPC[ | 0.736 | 0.755 | |
Normal Graph[ | 0.741 | 0.765 | |
PAD-SGMA | 0.754 | 0.766 |
D | AUC | D | AUC |
---|---|---|---|
1 | 0.745 | 5 | 0.748 |
2 | 0.754 | 6 | 0.743 |
3 | 0.749 | 7 | 0.742 |
4 | 0.747 |
Tab. 2 Influence of extended association distance of skeleton graph on detection performance
D | AUC | D | AUC |
---|---|---|---|
1 | 0.745 | 5 | 0.748 |
2 | 0.754 | 6 | 0.743 |
3 | 0.749 | 7 | 0.742 |
4 | 0.747 |
是否骨架图划分 | AUC |
---|---|
无 | 0.736 |
有 | 0.747 |
Tab. 3 Influence of skeleton graph division on detection performance
是否骨架图划分 | AUC |
---|---|
无 | 0.736 |
有 | 0.747 |
是否引入混合注意力 | AUC | 每轮训练时间/s |
---|---|---|
无 | 0.748 | 2 536 |
有 | 0.754 | 2 961 |
Tab. 4 Influence of mixed attention on detection performance
是否引入混合注意力 | AUC | 每轮训练时间/s |
---|---|---|
无 | 0.748 | 2 536 |
有 | 0.754 | 2 961 |
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