Dual generators and dual discriminators generative adversarial network for video anomaly detection

Abstract

Video anomaly detection (VAD) has garnered substantial attention from researchers due to its broad applications, including fire detection, drop detection, and vibration detection. In the current context of VAD, existing methods prioritize detection efficiency but overlook the impact of motion and appearance information. Additionally, achieving accurate predictions while retaining motion and appearance information poses a significant challenge. This paper proposes a novel semi-supervised method for VAD based on Generative Adversarial Network (GAN) structures with dual generators and dual discriminators, namely Dual-GAN. The future frame generator utilizes an improved encoder-decoder network to preserve more spatial information. Motion information for the future flow generator is obtained by estimating optical flow between reconstruction frames, complementing the optical flow between prediction frames. The introduction of a frame discriminator and a motion discriminator against the frame generator enhances the realism of prediction frames, which facilitates the identification of unexpected abnormal events. This method significantly outperforms comparative approaches in synthesizing video frames and predicting future flows, showcasing its effectiveness in handling diverse video data. Extensive experiments are performed on four publicly available datasets to ensure a comprehensive evaluation of the model performance. Further exploration could include refining the model architecture, exploring additional datasets, and adapting the methodology to specific application domains.