Research Article
[Retracted] An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods
| | Method | Classes | Experiment 1 | Experiment 2 | Experiment 3 | Experiment 4 | Experiment 5 | | | Weizmann | KTH | Weizmann | KTH | Weizmann | KTH | Weizmann | KTH | Weizmann | KTH |
| | Linear-SVM | C1
C2
C3
C4
C5
C6 | 1.00
0.99
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
0.98
1.00 | 1.00
0.99
1.00
0.99
1.00 | 1.00
0.98
1.00
0.99
1.00
1.00 | 1.00
0.98
0.99
0.98
1.00 | 1.00
0.90
1.00
0.98
1.00
1.00 | | Cubic-SVM | C1
C2
C3
C4
C5
C6 | 1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
1.00 | 1.00
1.00
1.00
1.00
0.99
1.00 | 1.00
0.99
1.00
0.99
1.00 | 1.00
0.98
1.00
0.99
0.99
1.00 | 1.00
0.98
0.99
0.98
1.00 | 1.00
0.90
1.00
0.98
1.00
0.99 | | Complex tree | C1
C2
C3
C4
C5
C6 | 0.98
0.87
0.94
0.90
0.97 | 1.00
0.98
1.00
0.99
0.98
1.00 | 0.99
0.89
0.93
0.88
0.95 | 1.00
0.96
1.00
0.98
0.96
1.00 | 0.98
0.85
0.94
0.90
0.97 | 1.00
0.98
1.00
0.99
0.98
1.00 | 0.98
0.88
0.94
0.88
0.96 | 0.99
0.99
0.99
0.99
1.00
1.00 | 0.98
0.87
0.94
0.87
0.96 | 0.99
0.99
0.99
0.96
1.00
1.00 | | Fine-KNN | C1
C2
C3
C4
C5
C6 | 1.00
0.99
0.99
0.99
1.00 | 0.98
1.00
0.98
0.81
1.00
0.98 | 0.93
0.87
0.91
0.92
1.00 | 0.97
1.00
0.99
0.69
1.00
0.96 | 0.79
0.63
0.71
0.76
0.99 | 0.98
0.99
0.98
0.64
1.00
0.94 | 0.69
0.51
0.65
0.61
0.89 | 0.95
0.88
0.91
0.61
0.76
0.91 | 0.60
0.50
0.62
0.51
0.71 | 0.75
0.54
0.76
0.50
0.54
0.81 | | Subspace-KNN | C1
C2
C3
C4
C5
C6 | 1.00
0.98
1.00
0.99
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
0.98
0.99
0.99
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
0.98
1.00
0.99
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
0.98
0.99
0.99
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 | 1.00
0.98
1.00
0.99
1.00 | 1.00
1.00
1.00
1.00
1.00
1.00 |
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