Video anomaly detection with both normal and anomaly memory modules

Abstract

In this paper, we propose a novel framework for video anomaly detection that employs dual memory modules for both normal and anomaly patterns. By maintaining separate memory modules, one for normal patterns and one for anomaly patterns, our approach captures a broader range of video data behaviors. By exploring separate memory modules for normal and anomaly patterns, we begin by generating pseudo-anomalies using a temporal pseudo-anomaly synthesizer. This data is then used to train the anomaly memory module, while normal data trains the normal memory module. To distinguish between normal and anomalous data, we introduce a loss function that computes memory loss between the two memory modules. We enhance the memory modules by incorporating entropy loss and a hard shrinkage rectified linear unit (ReLU). Additionally, we integrate skip connections within our model to ensure the memory module captures comprehensive patterns beyond prototypical representations. Extensive experimentation and analysis on various challenging video anomaly datasets validate the effectiveness of our approach in detecting anomalies. The code for our method is available at https://github.com/SVIL2024/Pseudo-Anomaly-MemAE.