Dual contrast discriminator with sharing attention for video anomaly detection

The detection of video anomalies is a well-known issue in the realm of visual research. The volume of normal and abnormal sample data in this field is unbalanced, hence unsupervised training is generally used in research. Since the development of deep learning, the field of video anomaly has developed from reconstruction-based detection methods to prediction-based detection methods, and then to hybrid detection methods. To identify the presence of anomalies, these methods take advantage of the differences between ground-truth frames and reconstruction or prediction frames. Thus, the evaluation of the results is directly impacted by the quality of the generated frames. Built around the Dual Contrast Discriminator for Video Sequences (DCDVS) and the corresponding loss function, we present a novel hybrid detection method for further explanation. With less false positives and more accuracy, this method improves the discriminator’s guidance on the reconstruction-prediction network’s generation performance. we integrate optical flow processing and attention processes into the Auto-encoder (AE) reconstruction network. The network’s sensitivity to motion information and its ability to concentrate on important areas are improved by this integration. Additionally, DCDVS’s capacity to successfully recognize significant features gets improved by introducing the attention module implemented through parameter sharing. Aiming to reduce the risk of network overfitting, we also invented reverse augmentation, a data augmentation technique designed specifically for temporal data. Our approach achieved outstanding performance with AUC scores of 99.4, 92.9, and 77.3 % on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets, respectively, demonstrates competitiveness with advanced methods and validates its effectiveness.