# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 4 Experimental Results
### Experiment V: Comparison with Other State-of-the-Art Methods  
For all three datasets that are used for testing, the interaction recognition accuracies achieved by the proposed system are compared with other state-of-the-art methods that were evaluated on the same datasets. The mean interaction recognition accuracy is computed by dividing the number of correct predictions by the total number of predictions that were made by the classifier as shown in Equation (23).  
\[C_{Accuracy}=\frac{\text{Correct Predictions}}{\text{Total Predictions}}\times 100\% \tag{23}\]  
Table 9 shows the results of the proposed system with some other state-of-the-art HOIR methods that were evaluated on one or more of the three datasets used in this research. The accuracy scores highlight that the proposed system outperforms all of them by a good margin.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**HOI Class**} & \multicolumn{3}{c}{**CNN**} & \multicolumn{3}{c}{**FCN**} & \multicolumn{3}{c}{**GCN**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline DR & 0.88 & 0.87 & 0.87 & 0.91 & 0.91 & 0.91 & 0.92 & 0.93 & 0.92 \\ ET & 0.88 & 0.88 & 0.88 & 0.91 & 0.91 & 0.91 & 0.92 & 0.93 & 0.92 \\ RB & 0.85 & 0.86 & 0.85 & 0.88 & 0.89 & 0.88 & 0.91 & 0.92 & 0.91 \\ WP & 0.87 & 0.88 & 0.87 & 0.89 & 0.91 & 0.90 & 0.93 & 0.93 & 0.93 \\ UL & 0.89 & 0.89 & 0.89 & 0.89 & 0.91 & 0.90 & 0.93 & 0.93 & 0.93 \\ PG & 0.85 & 0.86 & 0.85 & 0.87 & 0.88 & 0.87 & 0.92 & 0.92 & 0.92 \\ CC & 0.84 & 0.85 & 0.84 & 0.89 & 0.89 & 0.89 & 0.90 & 0.91 & 0.90 \\ UV & 0.91 & 0.91 & 0.91 & 0.91 & 0.93 & 0.92 & 0.94 & 0.96 & 0.95 \\ PR & 0.85 & 0.85 & 0.85 & 0.89 & 0.91 & 0.90 & 0.92 & 0.93 & 0.92 \\ LS & 0.91 & 0.91 & 0.91 & 0.92 & 0.93 & 0.92 & 0.95 & 0.96 & 0.95 \\ Mean & 0.87 & 0.88 & 0.87 & 0.90 & 0.91 & 0.90 & 0.92 & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of GCN with CNN and FCN classifiers on MSR Daily Activity dataset in terms of precision, recall and F1-score.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**HOI Class**} & \multicolumn{3}{c}{**CNN**} & \multicolumn{3}{c}{**FCN**} & \multicolumn{3}{c}{**GCN**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline RG & 0.87 & 0.89 & 0.88 & 0.91 & 0.91 & 0.91 & 0.91 & 0.92 & 0.91 \\ SF & 0.87 & 0.88 & 0.87 & 0.87 & 0.88 & 0.87 & 0.88 & 0.90 & 0.89 \\ TC & 0.83 & 0.83 & 0.83 & 0.85 & 0.85 & 0.85 & 0.87 & 0.87 & 0.87 \\ WM & 0.82 & 0.83 & 0.82 & 0.85 & 0.86 & 0.85 & 0.91 & 0.91 & 0.91 \\ MW & 0.86 & 0.85 & 0.85 & 0.88 & 0.89 & 0.88 & 0.91 & 0.92 & 0.91 \\ DW & 0.87 & 0.86 & 0.86 & 0.86 & 0.86 & 0.86 & 0.88 & 0.88 & 0.88 \\ LP & 0.82 & 0.82 & 0.82 & 0.85 & 0.87 & 0.86 & 0.89 & 0.89 & 0.89 \\ OV & 0.84 & 0.84 & 0.84 & 0.88 & 0.89 & 0.88 & 0.88 & 0.88 & 0.88 \\ Mean & 0.85 & 0.85 & 0.85 & 0.87 & 0.88 & 0.87 & **0.89** & **0.90** & **0.89** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of GCN with CNN and FCN classifiers on D3D HOI dataset in terms of precision, recall and F1-score.