Prior Knowledge Guided Network for Video Anomaly Detection-Reference-Cited by-同舟云学术

Prior Knowledge Guided Network for Video Anomaly Detection

Published:2023-12-06 Issue: Volume: Page:1-7
ISSN:
Container-title:ACM Multimedia Asia 2023
language:
Short-container-title:

Author:

Deng Zhewen¹^ORCID,Chen Dongyue²^ORCID,Deng Shizhuo³^ORCID

Affiliation:

1. College of Information Science and Engineering, Northeastern University, China

2. College of Information Science and Engineering, Northeastern University, China and Foshan Graduate School of Innovation, Northeastern University, China and National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, China

3. College of Information Science and Engineering, Northeastern University, China and Foshan Graduate School of Innovation, Northeastern University, China

Funder

Guangdong Basic and Applied Basic Research Foundation

The Fundamental Research Funds for the Central Universities

Innovation Fund of Chinese Universities Industry-University-Research

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3595916.3626421

Reference29 articles.

1. Hierarchical Scene Normality-Binding Modeling for Anomaly Detection in Surveillance Videos

2. Paul Bergmann , Michael Fauser , David Sattlegger , and Carsten Steger . 2019 . MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection . In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9592–9600 . Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2019. MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9592–9600.

3. Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

4. Appearance-Motion Memory Consistency Network for Video Anomaly Detection

5. Cascade R-CNN: Delving Into High Quality Object Detection