# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 4 Experimental Results
### Experiment IV: Comparison with Other Well-Known Classifiers  
In this experiment, the performance of the proposed system is analyzed by comparing the results obtained with GCN to the results of two other well-known classifiers, namely, the CNN [56] and fully convolutional network (FCN), on the same feature vector. The comparison of the performance is conducted in terms of precision, recall, and F1-score. This process is repeated for all three datasets that were mentioned before. Table 6 represents the comparison results over the Olympic Sports dataset while Table 7 shows the comparison using MSR Daily Activity 3D, and finally, Table 8 summarizes the comparison results achieved over the D3D HOI dataset.  
\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 BB & 0.87 & 0.88 & 0.87 & 0.93 & 0.93 & 0.93 & 0.95 & 0.97 & 0.96 \\ BL & 0.91 & 0.91 & 0.91 & 0.91 & 0.91 & 0.91 & 0.94 & 0.95 & 0.94 \\ TS & 0.92 & 0.92 & 0.92 & 0.93 & 0.94 & 0.93 & 0.95 & 0.96 & 0.95 \\ PT & 0.92 & 0.91 & 0.91 & 0.92 & 0.93 & 0.92 & 0.95 & 0.95 & 0.95 \\ DS & 0.89 & 0.90 & 0.89 & 0.89 & 0.90 & 0.89 & 0.93 & 0.94 & 0.93 \\ HR & 0.88 & 0.89 & 0.88 & 0.88 & 0.89 & 0.88 & 0.93 & 0.93 & 0.93 \\ JV & 0.89 & 0.89 & 0.89 & 0.91 & 0.91 & 0.91 & 0.92 & 0.94 & 0.93 \\ SP & 0.87 & 0.88 & 0.87 & 0.87 & 0.88 & 0.87 & 0.92 & 0.92 & 0.92 \\ SB & 0.87 & 0.87 & 0.87 & 0.87 & 0.87 & 0.87 & 0.91 & 0.92 & 0.91 \\ CJ & 0.88 & 0.88 & 0.88 & 0.89 & 0.89 & 0.89 & 0.92 & 0.93 & 0.92 \\ Mean & 0.89 & 0.89 & 0.89 & 0.90 & 0.91 & 0.90 & 0.93 & 0.94 & 0.94 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**H**} & \multicolumn{3}{c}{BB = basketball, BL = bowling, TS = tennis-serve, PT = platform, DS = discus, HR = hammer, JV = javelin, SP = shot put, SB = spring board, CJ = clean jerk.  
\end{table}
Table 6: Comparison of GCN with CNN and FCN classifiers on Olympic Sports 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
**Part** & **BB** & **BL** & **TS** & **PT** & **JV** & **SP** & **SB** & **CJ** & **AVG** \\ \hline HD & 93.21 & 90.34 & 90.03 & 90.12 & 92.24 & 93.4 & 94.32 & 90.45 & 91.764 \\ RE & 92.23 & 93.03 & 92.12 & 90.11 & 92.35 & 96.05 & 92.35 & 94.32 & 92.820 \\ LE & 86.29 & 95.67 & 91.78 & 94.38 & 96.05 & 84.12 & 93.62 & 92.23 & 91.768 \\ RH & 91.45 & 90.51 & 91.63 & 95.67 & 86.45 & 94.38 & 90.56 & 93.27 & 91.740 \\ LFI & 97.59 & 90.12 & 95.67 & 91.45 & 92.35 & 97.59 & 92.03 & 82.32 & 92.390 \\ NK & 94.38 & 96.05 & 94.38 & 95.67 & 85.23 & 82.75 & 91.14 & 92.23 & 91.479 \\ TRS & 93.62 & 92.72 & 95.67 & 87.24 & 94.32 & 95.67 & 93.35 & 95.67 & 93.533 \\ BTR & 92.23 & 95.67 & 97.59 & 92.23 & 94.38 & 83.03 & 90.42 & 92.23 & 92.223 \\ RK & 90.09 & 91.39 & 94.32 & 92.23 & 97.59 & 92.23 & 93.24 & 95.67 & 93.345 \\ LK & 88.43 & 97.59 & 92.23 & 95.67 & 94.32 & 82.45 & 90.76 & 94.32 & 91.971 \\ RF & 93.27 & 91.45 & 92.23 & 91.45 & 84.77 & 92.23 & 91.09 & 94.38 & 91.359 \\ LF & 94.38 & 92.35 & 95.67 & 94.38 & 93.27 & 93.27 & 93.03 & 92.35 & 93.588 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**H**} & \multicolumn{3}{c}{BB = basketball, BL = bowling, TS = tennis-serve, PT = platform, DS = discus, HR = hammer, JV = javelin, SP = shot put, SB = spring board, CJ = clean jerk.  
\end{table}
Table 5: Body part detection rate achieved over D3D-HOI dataset.  
As shown in the tables, GCN acts as the best classifier for all three datasets while FCN comes second in terms of precision, recall, and F1-scores.