A brief introduction to a framework named Multilevel Guidance-Exploration Network

TL;DR

This paper introduces MGENet, a novel anomaly detection framework that leverages high-level feature differences between guidance and exploration networks, achieving state-of-the-art results in human behavior anomaly detection.

Contribution

The paper proposes MGENet, a new framework using high-level feature differences and a guidance-exploration structure, distinct from reconstruction-based methods, for improved anomaly detection.

Findings

Achieves state-of-the-art performance on ShanghaiTech dataset.

Outperforms existing methods on UBnormal dataset.

Effectively detects scene-related behavioral anomalies.

Abstract

Human behavior anomaly detection aims to identify unusual human actions, playing a crucial role in intelligent surveillance and other areas. The current mainstream methods still adopt reconstruction or future frame prediction techniques. However, reconstructing or predicting low-level pixel features easily enables the network to achieve overly strong generalization ability, allowing anomalies to be reconstructed or predicted as effectively as normal data. Different from their methods, inspired by the Student-Teacher Network, we propose a novel framework called the Multilevel Guidance-Exploration Network(MGENet), which detects anomalies through the difference in high-level representation between the Guidance and Exploration network. Specifically, we first utilize the pre-trained Normalizing Flow that takes skeletal keypoints as input to guide an RGB encoder, which takes unmasked RGB frames as input, to explore motion latent features. Then, the RGB encoder guides the mask encoder, which takes masked RGB frames as input, to explore the latent appearance feature. Additionally, we design a Behavior-Scene Matching Module(BSMM) to detect scene-related behavioral anomalies. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on ShanghaiTech and UBnormal datasets.