# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 1 Introduction  
The proliferation of surveillance videos has driven the advances in video anomaly detection, which focuses on mining unusual patterns from these videos. In video anomaly detection, anomalies refer to events or behaviors that differ from normal scenes. Anomalies can take many forms in different environments:  
* Abnormal human behaviors, i.e., inappropriate behavior in public places, e.g., climbing, filming, or stealing, etc. * Vehicle abnormality, such as illegal parking, violating traffic rules, etc. * Abnormal environments, e.g., fire, explosion, building collapse, etc. * Equipment failure, such as surveillance camera failure, light changes, etc. These anomalies are the targets that are to be detected and identified by the video anomaly detection system. They are of great significance for security monitoring, traffic management, emergency management, and other fields. The determination of whether an event in a video is abnormal depends on the specific context of the scene. For instance, a truck might be considered abnormal on a university campus, but normal on a busy road. The problem of identifying anomalies arises from the vast variety of such events, which makes it challenging to gather enough training data to cover all possibilities. As a result, anomaly detection models often only learn to recognize normal activities that occur within a particular scene. Manually screening these videos is a labor-intensive process, so deep learning methods have become increasingly popular in video anomaly detection tasks [1]. Initially, these tasks utilized traditional machine learning techniques that relied on manual feature extraction and model building. However, with the advent and ongoing advancement of deep learning,video anomaly detection has evolved into a hybrid approach that combines both traditional machine learning and deep learning. The majority of current methods detect anomalies by extracting deep features and incorporating traditional machine learning techniques. In recent years, we have seen a proliferation of deep learning-based methods that have been continuously improved and refined. These methods can extract features from videos more effectively and efficiently and are better equipped to learn the normal distribution. Common deep learning-based video anomaly detection methods include reconstruction and prediction. In this paper, we propose a novel method of detecting anomalies by reconstructing video frames and calculating the reconstruction error, as demonstrated in Figure 1.  
In the field of video anomaly detection, autoencoders are commonly used deep learning networks for reconstructing video frames and extracting their latent features. However, autoencoders tend to result in blurry edges during reconstruction. To address this issue, we introduce the use of generative adversarial networks (GAN) as the main model structure, with the autoencoder serving as the generator. This ensures stable network training and improves the clarity of the reconstructed frames. To further enhance feature information relevance, we add a second-order channel attention module to the autoencoder, which learns the internal dependencies of features through second-order feature distributions, resulting in improved network focus on beneficial information and enhanced classification ability. Current methods often rely on training sets solely comprised of normal events, which can result in the model reconstructing abnormal frames during testing due to its strong reconstruction capabilities. To tackle this problem, we add another generator to the GAN, which transforms normal training frames into pseudo-abnormal frames [2]. This supplements the lack of abnormal frames in the original training set and enables the model to learn the pattern of abnormal events in advance. Our proposed method, referred to as the dual-generator generative adversarial network (DGGAN), features a dual-generator structure and incorporates GAN.