Research Article
Lightweight Fall Detection Algorithm Based on AlphaPose Optimization Model and ST-GCN
Table 1
GhostNet construction method diagram.
| Input | Operator | #exp | Out | SE | Stride |
| 4162 × 3 | Conv2d 3 × 3 | — | 16 | — | 2 | 2082 × 16 | GBN | 16 | 16 | — | 1 | 2082 × 16 | GBN | 48 | 24 | — | 2 | 1042 × 24 | GBN | 72 | 24 | — | 1 | 1042 × 24 | GBN | 72 | 40 | 1 | 2 | 522 × 40 | GBN | 120 | 40 | 1 | 1 | 522 × 40 | GBN | 240 | 80 | — | 2 | 262 × 80 | GBN | 200 | 80 | — | 1 | 262 × 80 | GBN | 184 | 80 | — | 1 | 262 × 80 | GBN | 184 | 80 | — | 1 | 262 × 80 | GBN | 480 | 112 | 1 | 1 | 262 × 112 | GBN | 672 | 112 | 1 | 1 | 262 × 112 | GBN | 672 | 160 | 1 | 2 | 132 × 160 | GBN | 960 | 160 | — | 1 | 132 × 160 | GBN | 960 | 160 | 1 | 1 | 132 × 160 | GBN | 960 | 160 | — | 1 | 132 × 160 | GBN | 960 | 160 | 1 | 1 | 132 × 160 | Conv2d 1 × 1 | — | 960 | — | 1 | 132 × 960 | AvgPool 7 × 7 | — | — | — | — | 12 × 960 | Conv2d 1 × 1 | — | 1280 | — | 1 | 12 × 1280 | FC | — | 1000 | — | — |
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