Influence-Aware Attention Networks for Anomaly Detection in Surveillance Videos-Reference-Cited by-同舟云学术

Influence-Aware Attention Networks for Anomaly Detection in Surveillance Videos

Published:2022-08 Issue:8 Volume:32 Page:5427-5437
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Zhang Sijia¹^ORCID,Gong Maoguo¹^ORCID,Xie Yu¹^ORCID,Qin A. K.²^ORCID,Li Hao¹^ORCID,Gao Yuan¹^ORCID,Ong Yew-Soon³^ORCID

Affiliation:

1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic Engineering, Xidian University, Xi’an, China

2. Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia

3. School of Computer Science and Engineering, Nanyang Technological University, Singapore

Funder

National Natural Science Foundation of China

Key Research and Development Program of Shaanxi Province

Australian Research Council

A*STAR Centre for Frontier AI Research

Data Science Artificial Intelligence Research Center (DSAIR) at the School of Computer Science and Engineering, Nanyang Technological University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/9849156/09701300.pdf?arnumber=9701300

Reference53 articles.

1. A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework

2. Abnormal Event Detection at 150 FPS in MATLAB

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5. Fast and Accurate Action Detection in Videos With Motion-Centric Attention Model

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