Table 1 The joint discriminator networks classification accuracy on synthetic datasets of respective joint rotations
From: Learning 3D joint constraints from vision-based motion capture datasets
Joint name [16] | Accuracy (%) | False positive (%) | False negative (%) | Optimal hidden layer |
|---|---|---|---|---|
Right up leg | 98.7 | 0.3 | 1.0 | 8 |
Right foot | 97.7 | 1 | 1.3 | 8 |
Left up leg | 98.9 | 0.3 | 0.8 | 8 |
Left foot | 98.2 | 0.6 | 1.2 | 8 |
Spine | 99.4 | 0.1 | 0.5 | 16 |
Spine1 | 99.4 | 0 | 0.6 | 16 |
Neck | 94.4 | 2.3 | 3.3 | 8 |
Head | 97.7 | 0.9 | 1.4 | 8 |
Left shoulder | 97.9 | 0.7 | 1.5 | 16 |
Left arm | 96.8 | 0.5 | 2.7 | 8 |
Left hand | 94.7 | 2.1 | 3.3 | 8 |
Right shoulder | 95.1 | 1.5 | 3.3 | 16 |
Right arm | 95.5 | 1.63 | 2.9 | 8 |
Right hand | 95 | 2 | 3 | 8 |