A brief introduction to a framework named Multilevel Guidance-Exploration Network
Guoqing Yang, Zhiming Luo, Jianzhe Gao, Yingxin Lai, Kun, Yang, Yifan He, Shaozi Li

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…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Human Pose and Action Recognition · Network Security and Intrusion Detection
