  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.
