Table 3.
Shows the accuracy of different machine learning classifiers on different sequences.
| No. | Classifiers | Frames Sequence | ||||
|---|---|---|---|---|---|---|
| 30 | 60 | 90 | 120 | 150 | ||
| 1 | FT | 45.2 | 60.3 | 47.0 | 69.0 | 46.8 |
| 2 | MT | 32.3 | 41.0 | 31.4 | 48.1 | 32.7 |
| 3 | CT | 20.8 | 27.7 | 21.4 | 27.2 | 19.5 |
| 4 | LD | 38.9 | 45.0 | 23.4 | 17.9 | 18.7 |
| 5 | GNB | 44.7 | 45.2 | 47.7 | 58.3 | 46.9 |
| 6 | KNB | 62.3 | 67.0 | 62.0 | 76.6 | 59.3 |
| 7 | LSVM | 53.5 | 73.6 | 53.5 | 78.0 | 48.9 |
| 8 | QSVM | 79.4 | 81.2 | 78.4 | 80.9 | 70.5 |
| 9 | CSVM | 81.3 | 82.0 | 78.3 | 82.4 | 71.9 |
| 10 | FGSVM | 82.4 | 81.1 | 79.5 | 80.8 | 72.9 |
| 11 | MGSVM | 80.0 | 82.2 | 76.1 | 82.2 | 70.1 |
| 12 | CGSVM | 51.1 | 63.9 | 43.4 | 77.9 | 41.8 |
| 13 | FKNN | 79.8 | 80.8 | 79.5 | 81.0 | 70.0 |
| 14 | MKNN | 79.2 | 80.3 | 77.6 | 81.8 | 69.1 |
| 15 | CRSKNN | 65.9 | 66.4 | 50.5 | 70.5 | 43.4 |
| 16 | CSNKNN | 81.6 | 82.1 | 75.1 | 79.4 | 69.8 |
| 17 | CBCKNN | 78.6 | 81.6 | 68.2 | 80.6 | 65.3 |
| 18 | WKNN | 79.0 | 81.1 | 72.3 | 80.9 | 65.6 |
| 19 | EBST | 45.0 | 57.3 | 46.3 | 64.4 | 48.8 |
| 20 | EBGT | 80.8 | 82.3 | 76.2 | 82.4 | 70.4 |
| 21 | ESD | 41.1 | 54.2 | 37.8 | 66.5 | 25.2 |
| 22 | ESKNN | 80.7 | 82.1 | 76.6 | 82.2 | 67.8 |
| 23 | ERUSBT | 42.5 | 46.1 | 47.1 | 57.4 | 43.2 |
| 24 | NNN | 70.9 | 76.1 | 70.8 | 81.4 | 63.4 |
| 25 | MNN | 76.3 | 81.6 | 77.9 | 82.8 | 70.9 |
| 26 | WNN | 80.6 | 82.2 | 79.2 | 81.8 | 75.1 |
| 27 | BNN | 73.9 | 79.0 | 71.3 | 80.0 | 62.2 |
| 28 | TNN | 70.6 | 81.3 | 72.3 | 82.2 | 58.6 |