OSAD: Open-set Supervised Anomaly Detection in Surveillance Videos based on Margin Metric Learning

Open-set Supervised Anomaly Detection (OSAD) strategy seeks to detect novel anomalies that are unseen during training. However, existing OSAD works fail to learn a comprehensive margin that separates normal and anomalous samples in the feature space owing to the lack of restriction on abnormal distribution. To this end, we propose a new OSAD scheme based on Margin Metric Learning, which fully exploits the differences between normal and abnormal events in the latent feature dimension. The developed framework embodies three major components. First, video clips including normal events and restricted anomalies are input into an Attention-embedded Spatial Convolutional Network to extract the spatial feature sequences. Then, the spatial feature sequences are input into a Temporal Convolutional Siamese Network to further obtain the temporal features. Second, a novel Quadruplet Contrastive Loss is designed based on the spatial-temporal features of normal and abnormal video clips to conduct the margin learning, which enlarges the inter-class distances between anomalous and normal instances as well as reduces the intra-class distances inside anomalous instances and normal instances. This contributes to acquire compact normal and abnormal distributions. Finally, during the testing stage, a simplified metric distance-based anomaly detection strategy is proposed to calculate the anomaly score of testing clip. Extensive experiments and ablation studies on the Avenue and ShanghaiTech datasets demonstrate the effectiveness and efficiency of our proposed method for discovering unseen anomalous events via limited types of aware anomalies.