Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

• URL: http://arxiv.org/abs/2404.04986v1
• Date: Sun, 7 Apr 2024 15:06:48 GMT
• Title: Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection
• Authors: Demetris Lappas, Vasileios Argyriou, Dimitrios Makris,
• Abstract summary: We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection. DDL combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy. Our approach adapts to the variability of normal and anomalous behaviors without fixed anomaly thresholds.
• Score: 8.957579200590985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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