Frame-Level Efficient Video Anomaly Detection Based on Prototype Networks

In this study, we present a novel approach to enhancing video anomaly detection by integrating an Adaptive Prototypical Network (APN) with an Enhanced Meta-Prototypical Network (EMPN) within a 3D-convolutional neural network (CNN) encoder-decoder architecture. Our method addresses the limitations of traditional anomaly detection methods by effectively capturing the intricate spatio-temporal dynamics inherent in video data. By dynamically updating representations of normal states, our model facilitates rapid adaptation to new and unseen scenes with minimal computational overhead. Furthermore, we introduce a post-processing step utilizing a median filter to refine anomaly scores, reducing noise and false positives. Extensive experiments conducted on various benchmark datasets demonstrate that our approach outperforms state-of-the-art methods on two datasets, showcasing its superior performance.