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, with AUC of 86.9 % and 73.5 %, respectively. The code will be available on https://github.com/molu-ggg/GENet.

人类行为异常检测通过指导和探索网络之间的高级表示差异来检测异常行为。通过利用预训练的正态流引导RGB编码器来探索运动潜在特征，然后RGB编码器指导遮罩编码器来探索外观潜在特征，并设计了行为场景匹配模块来检测与场景相关的行为异常。在上海科技和UBnormal数据集上实验证明了我们的方法达到了最先进的性能，分别为86.9％和73.5％的AUC。

用于人体行为异常检测的多层引导探索网络和行为场景匹配方法