Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow

TL;DR

This paper introduces a novel video anomaly detection method leveraging normality-granted optical flow to distinguish normal from abnormal events, focusing on predicting normal frames and extending appearance-motion correspondence.

Contribution

It proposes a duality-based optical flow approach and extends appearance-motion correspondence from reconstruction to prediction, improving anomaly detection accuracy.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively discriminates anomalies using normality-guided optical flow.

Enhances frame prediction with a margin loss.

Abstract

Video anomaly detection is a challenging task because of diverse abnormal events. To this task, methods based on reconstruction and prediction are wildly used in recent works, which are built on the assumption that learning on normal data, anomalies cannot be reconstructed or predicated as good as normal patterns, namely the anomaly result with more errors. In this paper, we propose to discriminate anomalies from normal ones by the duality of normality-granted optical flow, which is conducive to predict normal frames but adverse to abnormal frames. The normality-granted optical flow is predicted from a single frame, to keep the motion knowledge focused on normal patterns. Meanwhile, We extend the appearance-motion correspondence scheme from frame reconstruction to prediction, which not only helps to learn the knowledge about object appearances and correlated motion, but also meets the fact that motion is the transformation between appearances. We also introduce a margin loss to enhance the learning of frame prediction. Experiments on standard benchmark datasets demonstrate the impressive performance of our approach.