Deep spatio-temporal graph convolutional network for police combat action recognition and training assessment
Yan Wang · 2025
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Description
Traditional police combat training relies heavily on subjective evaluation by human instructors, which lacks consistency and comprehensive coverage of complex movement patterns in real-world scenarios. This paper presents an enhanced deep spatio-temporal graph convolutional network (ST-GCN) framework specifically designed for automated police combat action recognition and quality assessment.
Catalog record
| CREATOR | Yan Wang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |