Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder-Reference-Cited by-同舟云学术

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Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder

Published:2022 Issue: Volume:10 Page:44278-44289
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wang Lin¹^ORCID,Tan Haishu²,Zhou Fuqiang¹^ORCID,Zuo Wangxia³^ORCID,Sun Pengfei¹^ORCID

Affiliation:

1. School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, China

2. School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China

3. School of Electrical Engineering, University of South China, Hengyang, China

Funder

Ji Hua Laboratory of Guangdong Province, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09758677.pdf?arnumber=9758677

Reference48 articles.

1. Bayesian Inference in Econometric Models Using Monte Carlo Integration

2. Variational autoencoder based anomaly detection using reconstruction probability;an;Special lecture,2015

3. Auto-encoding variational Bayes;kingma;Proc 2nd Int Conf Learn Represent (ICLR),2014

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