Improved Video Anomaly Detection with Dual Generators and Channel Attention

Video anomaly detection is a crucial aspect of understanding surveillance videos in real-world scenarios and has been gaining attention in the computer vision community. However, a significant challenge is that the training data only include normal events, making it difficult for models to learn abnormal patterns. To address this issue, we propose a novel dual-generator generative adversarial network method that improves the model's ability to detect unknown anomalies by learning the anomaly distribution in advance. Our approach consists of a noise generator and a reconstruction generator, where the former focuses on generating pseudo-anomaly frames and the latter aims to comprehensively learn the distribution of normal video frames. Furthermore, the integration of a second-order channel attention module enhances the learning capacity of the model. Experiments on two popular datasets demonstrate the superiority of our proposed method and show that it can effectively detect abnormal frames after learning the pseudo-anomaly distribution in advance.