SHS Web Conf.
Volume 132, 2022Innovative Economic Symposium 2021 – New Trends in Business and Corporate Finance in COVID-19 Era (IES2021)
|Number of page(s)||8|
|Section||New Trends in Business and Corporate Finance in COVID-19 Era|
|Published online||05 January 2022|
Semi-supervised generative adversarial networks for anomaly detection
Department of Data Science, Kookmin University, 77 Jungrungro, Sungbukku, Seoul, 02707 South Korea
* Corresponding author: firstname.lastname@example.org
Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.
Key words: Generative adversarial network / Gamma correction / Computer vision / Anomaly detection
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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