Video Anomaly Detection Based on Random Masked Transformer

With the increasing popularity of various types of surveillance cameras, video anomaly event detection has become increasingly important. In this paper, we introduce a novel method for anomaly detection that utilizes a random masked transformer. This approach involves masking a portion of the video patches in the video sequence to be able to extract features using a temporal transformer. Besides, we design a temporal transformer block and a spatial transformer block to extract spatial-temporal features. Based on the spatial and temporal transformers, we define anomalies as those with large differences between the predicted frame and true frame. Furthermore, we propose to use the RGB difference to represent the motion, which is more efficient than the optical flow-based methods. Our experiments on public datasets demonstrate that the proposed method using a random masked transformer approach can detect anomalies from videos effectively.