期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2025-10-08 |
| 作品名稱 | Deploying a Skeleton-Based Video Anomaly Detection System on Edge Devices for Human Activity Surveillance |
| 作品名稱（其他語言） | |
| 著者 | Shao-Kang Huang; Wei-Yen Wang; Chen-Chien Hsu |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IEEE Embedded Systems Letters |
| 摘要 | 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. |
| 關鍵字 | Edge device;Normalizing flow;Raspberry pi 5;Video Anomaly Detection;Human activity surveillance |
| 語言 | zh_TW |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SCI Scopus |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128221 ) |