Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement

TL;DR

This paper introduces a deep learning-based unsupervised video anomaly detection method leveraging the 'normality advantage' property, combined with a self-paced refinement scheme, achieving superior performance and surpassing many existing methods.

Contribution

It presents the first deep reconstruction-based UVAD method exploiting normality advantage and introduces a novel self-paced refinement scheme to improve detection accuracy.

Findings

Outperforms existing UVAD methods by 5-9% AUROC

Sometimes surpasses classic VAD methods in performance

Demonstrates effectiveness of deep reconstruction for UVAD

Abstract

While classic video anomaly detection (VAD) requires labeled normal videos for training, emerging unsupervised VAD (UVAD) aims to discover anomalies directly from fully unlabeled videos. However, existing UVAD methods still rely on shallow models to perform detection or initialization, and they are evidently inferior to classic VAD methods. This paper proposes a full deep neural network (DNN) based solution that can realize highly effective UVAD. First, we, for the first time, point out that deep reconstruction can be surprisingly effective for UVAD, which inspires us to unveil a property named "normality advantage", i.e., normal events will enjoy lower reconstruction loss when DNN learns to reconstruct unlabeled videos. With this property, we propose Localization based Reconstruction (LBR) as a strong UVAD baseline and a solid foundation of our solution. Second, we propose a novel self-paced refinement (SPR) scheme, which is synthesized into LBR to conduct UVAD. Unlike ordinary self-paced learning that injects more samples in an easy-to-hard manner, the proposed SPR scheme gradually drops samples so that suspicious anomalies can be removed from the learning process. In this way, SPR consolidates normality advantage and enables better UVAD in a more proactive way. Finally, we further design a variant solution that explicitly takes the motion cues into account. The solution evidently enhances the UVAD performance, and it sometimes even surpasses the best classic VAD methods. Experiments show that our solution not only significantly outperforms existing UVAD methods by a wide margin (5% to 9% AUROC), but also enables UVAD to catch up with the mainstream performance of classic VAD.