Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection
This provides a scalable and adaptable solution for video surveillance challenges, though it appears incremental as it builds on existing pseudo-anomaly methods.
The paper tackled video anomaly detection by introducing Dynamic Distinction Learning, which uses pseudo-anomalies and dynamic weighting to improve accuracy, achieving superior performance on datasets like Ped2, Avenue, and ShanghaiTech.
We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy. By training on pseudo-anomalies, our approach adapts to the variability of normal and anomalous behaviors without fixed anomaly thresholds. Our model showcases superior performance on the Ped2, Avenue and ShanghaiTech datasets, where individual models are tailored for each scene. These achievements highlight DDL's effectiveness in advancing anomaly detection, offering a scalable and adaptable solution for video surveillance challenges.