Deploying a Skeleton-Based Video Anomaly Detection System on Edge Devices for Human Activity Surveillance

Recent advances in embedded computing have enabled edge devices to run AI models more efficiently, sparking interest in deploying video anomaly detection (VAD) systems for smart surveillance. However, practical implementation requires a careful balance between detection accuracy and computational efficiency. This letter proposes a novel and lightweight anomaly scoring model that integrates a normalizing flow with a multi-scale spatial temporal graph convolutional network (stGCN). The proposed model supports both unsupervised and supervised modes. To evaluate its deployment feasibility, we implement the full VAD pipeline—including YOLOv8n-Pose, BoT-SORT, and the proposed scoring model—on a Raspberry Pi 5. Experimental results demonstrate that our method achieves AUC scores of 86.2% and 72.2% on the ShanghaiTech and UBnormal datasets for unsupervised VAD, respectively, and an AUC score of 82.4% for supervised VAD on the UBnormal dataset, outperforming state-of-the-art methods.