Generative Neural Networks for Anomaly Detection in Crowded Scenes
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Tian | - |
dc.contributor.author | Qiao, Meina | - |
dc.contributor.author | Lin, Zhiwei | - |
dc.contributor.author | Li, Ce | - |
dc.contributor.author | Snoussi, Hichem | - |
dc.contributor.author | Liu, Zhe | - |
dc.contributor.author | Choi, Chang | - |
dc.date.available | 2020-10-20T06:44:21Z | - |
dc.date.created | 2020-06-10 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.issn | 1556-6013 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/78575 | - |
dc.description.abstract | Security surveillance is critical to social harmony and people's peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S-2-VAE, for anomaly detection from video data. The S-2-VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (S-F-VAE) and a Skip Convolutional VAE (S-C-VAE). The S-F-VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The S-C-VAE, as a key component of S(2-)VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both S-F-VAE and S-C-VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed S-2-VAE is evaluated using four public datasets. The experimental results show that the S-2-VAE outperforms the state-of-the-art algorithms. The code is available publicly at https://github.com/tianwangbuaa/. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY | - |
dc.title | Generative Neural Networks for Anomaly Detection in Crowded Scenes | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000457798900005 | - |
dc.identifier.doi | 10.1109/TIFS.2018.2878538 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, v.14, no.5, pp.1390 - 1399 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 1399 | - |
dc.citation.startPage | 1390 | - |
dc.citation.title | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY | - |
dc.citation.volume | 14 | - |
dc.citation.number | 5 | - |
dc.contributor.affiliatedAuthor | Choi, Chang | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Spatio-temporal | - |
dc.subject.keywordAuthor | anomaly detection | - |
dc.subject.keywordAuthor | variational autoencoder | - |
dc.subject.keywordAuthor | loss function | - |
dc.subject.keywordPlus | ABNORMAL EVENT DETECTION | - |
dc.subject.keywordPlus | LOCALIZATION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | MODEL | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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