Detecting Anomalies in Videos using Perception Generative Adversarial Network

This paper presents a novel end-to-end unsupervised deep learning approach for video anomaly detection. We propose to utilize the Perception Generative Adversarial Net (Perception-GAN), which is trained using the initial portion of the video. The generator of the perceptual-GAN learns how to generate events similar to the normal events, while the discriminator of the perceptual-GAN learns how to distinguish the abnormal events from the normal events. At testing time, only the discriminator is used to solve our discriminative task (abnormality detection). Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve the anomaly detection task. A two-stream networks framework and an update strategy is employed to improve the detection result. We test our approach on three popular benchmarks and the experimental results verify the superiority of our method compared to the state of the arts.