Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection

TL;DR

This paper introduces a novel video anomaly detection method that uses appearance blur and motion-guided memory to enable zero-shot cross-dataset validation, improving detection without domain adaptation.

Contribution

It proposes a motion-guided memory module combined with appearance blurring and residual attention to enhance cross-dataset anomaly detection in videos.

Findings

Effective in cross-dataset validation without domain adaptation

Outperforms existing methods on benchmark datasets

Utilizes motion memory to distinguish normal and abnormal motions

Abstract

Video anomaly detection (VAD) often learns the distribution of normal samples and detects the anomaly through measuring significant deviations, but the undesired generalization may reconstruct a few anomalies thus suppressing the deviations. Meanwhile, most VADs cannot cope with cross-dataset validation for new target domains, and few-shot methods must laboriously rely on model-tuning from the target domain to complete domain adaptation. To address these problems, we propose a novel VAD method with a motion-guided memory module to achieve cross-dataset validation with zero-shot. First, we add Gaussian blur to the raw appearance images, thereby constructing the global pseudo-anomaly, which serves as the input to the network. Then, we propose multi-scale residual channel attention to deblur the pseudo-anomaly in normal samples. Next, memory items are obtained by recording the motion features in the training phase, which are used to retrieve the motion features from the raw information in the testing phase. Lastly, our method can ignore the blurred real anomaly through attention and rely on motion memory items to increase the normality gap between normal and abnormal motion. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed method. Compared with cross-domain methods, our method achieves competitive performance without adaptation during testing.