A Promotion Method for Generation Error Based Video Anomaly Detection

TL;DR

This paper introduces a novel promotion method for video anomaly detection that uses maximum block-level generation errors to improve detection accuracy, addressing limitations of traditional frame-level GE methods.

Contribution

The paper proposes a block-level GE promotion method that enhances anomaly saliency detection and adapts to varying normal-GE levels across videos, achieving state-of-the-art results.

Findings

Improved anomaly detection accuracy on multiple datasets.

Effective suppression of normal-area GEs in anomaly detection.

State-of-the-art performance achieved with the proposed method.

Abstract

Surveillance video anomaly detection is to detect events that rarely or never happened in a certain scene. The generation error (GE)-based methods exhibit excellent performance on this task. They firstly train a generative neural network (GNN) to generate normal samples, then judge the samples with large GEs as anomalies. Almost all the GE-based methods utilize frame-level GEs to detect anomalies. However, anomalies generally occur in local areas, the frame-level GE introduces GEs of normal areas to anomaly discriminations, that brings two problems: i) The GE of normal areas reduces the anomaly saliency of the anomalous frame. ii) Different videos have different normal-GE-levels, thus it is hard to set a uniform threshold for all videos to detect anomalies. To address these problems, we propose a promotion method: utilize the maximum of block-level GEs on the frame to detect anomaly. Firstly, we calculate the block-level GEs at each position on the frame. Then, we utilize the maximum of the block-level GEs on the frame to detect anomalies. Based on the existed GNN models, experiments are carried out on multiple datasets. The results demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance.