VATMAN: Video Anomaly Transformer for Monitoring Accidents and Nefariousness

Video anomaly detection involves automatically identifying unusual or abnormal events in videos, such as crimes and accidents. This paper proposes a novel framework for video anomaly detection named Video Anomaly Transformer for Monitoring Accidents and Nefariousness (VATMAN). Existing approaches often implicitly train the model to generalize the normal data into a single distribution, then detect data that are less generalized as anomalies during the evaluation. However, such methods often struggle with complex data. To address this, our framework leverages the self-attention mechanisms of Transformers, combined with pre-trained 3D Convolutional Neural Networks (3DCNNs). The proposed anomaly score based on the self-attention mechanism can achieve anomaly detection that is less sensitive to data complexity. Experiments on the Abnormal Behavior CCTV Video Dataset demonstrate that VATMAN outperforms existing anomaly detection methods and shows many favorable properties.