Semi-supervised generative adversarial networks for anomaly detection

Department of Data Science, Kookmin University, 77 Jungrungro, Sungbukku, Seoul, 02707 South Korea

^* Corresponding author: ychung@kookmin.ac.kr

Abstract

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.