Memory-enhanced appearance-motion consistency framework for video anomaly detection

Ning, Zhiyuan; Wang, Zile; Liu, Yang; Liu, Jing; Song, Liang

doi:10.1016/j.comcom.2024.01.004

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Other1

Preprint1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Channel based approach via faster dual prediction network for video anomaly detection

Li,

Shen,

Sun

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

Channel based approach via faster dual prediction network for video anomaly detection

Li,

Shen,

Sun

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection

Hussain,

Ullah,

Khan

et al. 2024

Expert Systems with Applications

View full text Add to dashboard Cite

Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

Lappas,

Argyriou,

Makris

2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

View full text Add to dashboard Cite

We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy. By training on pseudoanomalies, our approach adapts to the variability of normal and anomalous behaviors without fixed anomaly thresholds. Our model showcases superior performance on the Ped2, Avenue and ShanghaiTech datasets, where individual models are tailored for each scene. These achievements highlight DDL's effectiveness in advancing anomaly detection, offering a scalable and adaptable solution for video surveillance challenges. Our work can be found on: https://github.com/demetrislappas/DDL.git

show abstract

Memory-enhanced appearance-motion consistency framework for video anomaly detection

Cited by 5 publications

References 18 publications

Channel based approach via faster dual prediction network for video anomaly detection

Channel based approach via faster dual prediction network for video anomaly detection

TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection

Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

Contact Info

Product

Resources

About