Human abnormal behavior detection in intelligent surveillance scenarios faces dual challenges: complex spatiotemporal feature coupling and limited edge computing resources. To address these issues, this paper proposes a lightweight and efficient anomaly detection framework, namely the Spatiotemporal Graph Bidirectional Autoencoder (ST-GBAE). This method abandons the conventional computationally intensive RGB pixel-based approaches. Instead, it leverages human skeleton data. Graph Convolutional Networks (GCN) are employed to explicitly model the skeletal topology in non-Euclidean space. Bidirectional Long Short-Term Memory networks (Bi-LSTM) are cascaded to capture global causal dependencies of action sequences along the temporal dimension. The model adopts an unsupervised learning paradigm. It constructs a latent manifold by minimizing the reconstruction error of normal behaviors. Subsequently, unknown anomalies are precisely identified through the reconstruction failure mechanism. Extensive experiments on the ShanghaiTech benchmark dataset demonstrate that ST-GBAE achieves an AUC of 0. 965, reaching accuracy levels comparable to state-of-the-art heavy Transformer models. Moreover, with only 1. 85 M parameters, it attains a real-time inference speed of 172 FPS, approximately 4 \ (\) faster than comparable methods. These results confirm the effectiveness of joint spatiotemporal modeling and establish the significant advantages of this method in balancing accuracy and efficiency. This work provides a valuable solution for deploying large-scale real-time intelligent security systems.
Hong et al. (Sat,) studied this question.
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