Dynamic thresholding for video anomaly detection

Abstract

Anomaly detection is one of the most important applications in video surveillance that involves the temporal localisation of anomaly events in unannotated video sequences. By learning the normal patterns to generate frames and calculating their reconstruction error relative to the ground truth, a frame can be recognised as being abnormal if the reconstruction error exceeds a threshold. Most existing works use a fixed threshold that computes over all the testing data to determine the anomalies. However, fixed threshold strategy cannot address the challenges brought by the dynamic environment, e.g. changes in illumination conditions. In this paper, a dynamic thresholding algorithm (DTA) is proposed, which is fully data-driven and capable of automatically determining thresholds such that the developed anomaly detection system can flexibly adapt to different scenarios. The proposed DTA is independent of the backbone network and can be easily incorporated into most existing video anomaly detection models to help identify the appropriate thresholds. On both synthetic and real-world datasets, the experimental results show that with the proposed DTA, the video anomaly detection methods achieve a better performance considering the changes in dynamic environment.