Detecting Anomaly Event in Video Based on Generative Adversarial Network

Anomaly detection in videos is a challenging computer vision problem. Existing state-of-the-art video anomaly detection methods mainly focus on the structural design of deep neural networks to obtain performance improvements. Different from the main research trend, this paper focuses on combining ensemble learning and deep neural networks and proposes an approach based on ensemble generative adversarial network (GAN). In the proposed method, a set of generators and a set of discriminators are trained together, so each generator gets feedback from multiple discriminators and vice versa. Compared with a single GAN, the proposed ensemble GAN can better model the distribution of normal data to better detect anomalies. In the experiments, the performance of the proposed method is tested on two public datasets. The results show that ensemble learning significantly improves the performance of a single detection model, which outperforms some existing state-of-the-art methods.