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

• URL: http://arxiv.org/abs/2312.04119v3
• Date: Sun, 9 Jun 2024 07:12:44 GMT
• Title: A brief introduction to a framework named Multilevel Guidance-Exploration Network
• Authors: Guoqing Yang, Zhiming Luo, Jianzhe Gao, Yingxin Lai, Kun Yang, Yifan He, Shaozi Li,
• Abstract summary: 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. Our proposed method achieves state-of-the-art performance on ShanghaiTech and UBnormal datasets.
• Score: 23.794585834150983
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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