Dual-Scale Temporal Dependency Learning for Unsupervised Video Anomaly Detection

Video anomaly detection plays an increasingly crucial role in intelligent surveillance systems. Inspired by previous unsupervised methods, this paper focuses on detecting frame-level anomalies with long-term temporal dependencies. To this end, we propose a dual-scale temporal dependency learning method for video anomaly detection model, which consists of two main modules: a single-frame reconstruction module and a multi-frame feature enhancement module, processed end-to-end without relying on any pre-trained models. To validate the proposed approach, we introduce a new Elevator dataset containing various types of remote temporal dependency anomalies. Experimental results on the self-constructed Elevator dataset and two benchmarks demonstrate the effectiveness of our proposed approach.