Anomaly detection in video is one of the challenging computer vision problems. The 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 article focuses on the combination of ensemble learning and deep neural network, and proposes a method based on ensemble generative adversarial networks (GAN). In the proposed method, a set of generators and discriminators are trained together, so each generator gets feedback from multiple discriminators, and vice versa. Compared with a single GAN, an ensemble GAN can better model the distribution of normal data, thereby better detecting anomalies. The performance of the proposed method is tested on two public data sets. The results show that ensemble learning significantly improves the performance of a single detection model, and the performance of ensemble GAN exceeds the frame-level AUC of 0.4% and 0.3% on the two data sets compared with the existing recent methods, respectively.