Memory-enhanced appearance-motion consistency framework for video anomaly detection-Reference-Cited by-同舟云学术

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

Published:2024-02 Issue: Volume:216 Page:159-167
ISSN:0140-3664
Container-title:Computer Communications
language:en
Short-container-title:Computer Communications

Author:

Ning Zhiyuan^ORCID,Wang Zile,Liu Yang,Liu Jing^ORCID,Song Liang^ORCID

Publisher

Elsevier BV

Reference46 articles.

1. Collaborative normality learning framework for weakly supervised video anomaly detection;Liu;IEEE Trans. Circuits Syst. II,2022

2. Deep appearance features for abnormal behavior detection in video;Smeureanu,2017

3. Normality learning in multispace for video anomaly detection;Zhang;IEEE Trans. Circuits Syst. Video Technol.,2020

4. Clustering driven deep autoencoder for video anomaly detection;Chang,2020

5. Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models;Liu,2023

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection;Expert Systems with Applications;2024-12

2. Channel based approach via faster dual prediction network for video anomaly detection;Multimedia Tools and Applications;2024-03-04

3. Unsupervised Video Anomaly Detection for Multiple Traffic Scenes Via Memory-Guided Frame Prediction and Reconstruction;2024

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