Video Anomaly Detection by Fusion of Middle Layer Features of U-Net Discriminator in Spatio-Temporal Generative Adversarial Networks
Satoshi Hashimoto, Kenichi Kudo, Takayuki TAKAHASHI, Kazunori Umeda · Journal of the Japan Society for Precision Engineering · 2021
In recent years, attempts to detect video anomalies in daily life, manufacturing sites, etc. using deep learning have been investigated. In particular, generative adversarial networks (GANs) have been widely used. However, they are inefficient, and simple difference-based methods are affected by noise. This paper proposes a new unsupervised learning method for video anomaly detection using spatio-temporal generative adversarial networks. Since the proposed method utilizes the middle layer features of the U-Net Discriminator, it is able to detect anomalies efficiently and accurately in the region of interest of the Discriminator.