Memory Reconstruction Based Dual Encoders for Anomaly Detection

Anomaly detection technology relying on memory reconstruction leverages the difference in reconstruction errors between the normal and abnormal frames to achieve superior detection performance. However, there are still some challenges with this technology. First, the memory has insufficient representation capacity for features. Second, there is a contradiction between feature fusion and reconstruction. As feature fusion copies the abnormal patterns into the reconstructed frames, the abnormal frames are effectively reconstructed, reducing the detection performance. In response to these challenges, we use a memory update threshold to improve the representational power of memory. We also propose a dual-encoder anomaly detection model to restrict anomaly feature propagation. Experiment results demonstrate the effectiveness and robustness of our approach.