GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection

TL;DR

This paper introduces 'glance annotation', a cost-effective labeling method for video anomaly detection, and proposes GlanceVAD, a model leveraging these annotations to improve detection accuracy while reducing labeling effort.

Contribution

The paper presents a novel glance annotation paradigm and a tailored GlanceVAD method that effectively utilize these annotations for improved anomaly detection performance.

Findings

Glance annotation reduces labeling cost significantly.

GlanceVAD outperforms existing unsupervised and weakly supervised methods.

The approach achieves a good balance between annotation effort and detection accuracy.

Abstract

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite significant progress, these methods still suffer from unsatisfactory results such as numerous false alarms, primarily due to the absence of precise temporal anomaly annotation. In this paper, we present a novel labeling paradigm, termed "glance annotation", to achieve a better balance between anomaly detection accuracy and annotation cost. Specifically, glance annotation is a random frame within each abnormal event, which can be easily accessed and is cost-effective. To assess its effectiveness, we manually annotate the glance annotations for two standard video anomaly detection datasets: UCF-Crime and XD-Violence. Additionally, we propose a customized GlanceVAD method, that leverages gaussian kernels as the basic unit to compose the temporal anomaly distribution, enabling the learning of diverse and robust anomaly representations from the glance annotations. Through comprehensive analysis and experiments, we verify that the proposed labeling paradigm can achieve an excellent trade-off between annotation cost and model performance. Extensive experimental results also demonstrate the effectiveness of our GlanceVAD approach, which significantly outperforms existing advanced unsupervised and weakly supervised methods. Code and annotations will be publicly available at https://github.com/pipixin321/GlanceVAD.