Multi-Channel Generative Framework and Supervised Learning for Anomaly Detection in Surveillance Videos

Recently, most state-of-the-art anomaly detection methods are based on apparent motion and appearance reconstruction networks and use error estimation between generated and real information as detection features. These approaches achieve promising results by only using normal samples for training st...

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Detalles Bibliográficos
Autores principales: Vu, Tuan-Hung, Boonaert, Jacques, Ambellouis, Sebastien, Taleb-Ahmed, Abdelmalik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124646/
https://www.ncbi.nlm.nih.gov/pubmed/34063625
http://dx.doi.org/10.3390/s21093179

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124646/
https://www.ncbi.nlm.nih.gov/pubmed/34063625
http://dx.doi.org/10.3390/s21093179

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