Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

Demetris Lappas, Vasileios Argyriou, Dimitrios Makris; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3961-3970

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.