  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.
