Semi-supervised generative adversarial networks for anomaly detection

Advancements in security have provided ways of recording anomalies daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types crimes on videos. Additionally, we intend tackle one most recurring difficulties anomaly detection: illumination. this, propose light augmentation algorithm based gamma correction help networks its classification task. The proposed process performs slightly better than other models.