Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

The unpredictable nature of anomalies further adds to the complexity, We introduce Dynamic Distinction Learning (DDL) for making it difficult for models trained on'normal' behavior Video Anomaly Detection, a novel video anomaly detection to generalize and identify outliers effectively. This difficulty methodology that combines pseudo-anomalies, dynamic is magnified by the context-sensitive definition of what constitutes anomaly weighting, and a distinction loss function an anomaly within video data, as it can vary significantly to improve detection accuracy.