# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 4 Results and Discussion
### Comparison with the State-of-the-Art Methods  
This section presents a comprehensive quantitative comparison between our proposed DA-R3 DCNN model and state-of-the-art methods for human action recognition. The comparisons were based on average accuracy and were conducted on the UCF11, UCF50, HMDB51, and UCF101 datasets, as shown in Tables 3, 4, 5, and 6, respectively. Table 3 showcases the results that indicate that our proposed DA-R3 DCNN achieved the highest accuracy of 98.6%, surpassing all other methods. The Fusion-based discriminative features method [31] came in second place, with an accuracy of 97.8%. Among the comparative methods, the lowest accuracy on the UCF11 dataset was obtained by the Local-global features + QSVM method [32], which achieved an accuracy of 82.6%. The rest of the comparative methods included Multi-task hierarchical clustering [33], BT-LSTM [34], Deep autoencoder [35], Two-stream attention LSTM [36], Weighted entropy-variances-based feature selection [37], Dilated CNN + BiLSTM + RB [38], DS-GRU [39], Squeezed CNN [40], BS-2 SCN [41], and 3 DCNN [42]. These methods achieved accuracies of 89.7%, 85.3%, 96.2%, 96.9%, 96.9%, 94.5%, 89.0%, 97.1%, 87.4%, 90.1%, and 85.1%, respectively. Based on the comparative assessment, the proposed DA-R3 DCNN achieved an average accuracy improvement of 8.47% as compared to the average results of state-of-the-art methods on the UCF11 dataset. For the UCF50 dataset, the results presented in Table 4 validate that the proposedDA-R3 DCNN framework achieved the best results by attaining an accuracy of 97.4%, followed by the Deep autoencoder [35] method, which obtained a runner-up accuracy of 96.4%. Among all the comparative methods on the UCF50 dataset, the Local-global features + QSVM method [32] achieved the lowest accuracy of 69.4%. The other methods in the comparison included Multi-task hierarchical clustering [33], Ensemble model with swarm-based optimization [43], DS-GRU [39], Hybrid Deep Evolving Neural Networks [44], ViT + Multi Layer LSTM [45], and 3 DCNN [42], which achieved accuracies of 93.2%, 92.2%, 95.2%, 77.3%, 96.1%, and 82.6%, respectively. Upon analyzing the comparative results presented in Table 4, it is evident that the proposed DA-R3 DCNN exhibited an average accuracy improvement of 10.93% over the average results of the state-of-the-art methods on the UCF50 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{2-3}  & **Without Dual Attention** & **With Dual Attention** \\ \hline UCF11 & 93.1 & 98.6 \\ UCF50 & 91.8 & 97.4 \\ HMDB51 & 77.2 & 82.5 \\ UCF101 & 93.6 & 97.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average accuracies obtained by our proposed framework with and without the dual-attention module on UCF11, UCF50, HMDB51, and UCF101 datasets. Figure 5: Confusion matrices computed for the proposed DA-R3 DCNN framework for the test sets of four experimented datasets: (**a**) UCF11 dataset, (**b**) UCF50 dataset, (**c**) HMDB51 dataset, and (**d**) UCF101 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Year** & **Accuracy (\%)** \\ \hline Multi-task hierarchical clustering [33] & 2017 & 93.2 \\ Deep autoencoder [35] & 2019 & 96.4 \\ Ensemble model with swarm-based optimization [43] & 2021 & 92.2 \\ DS-GRU [39] & 2021 & 95.2 \\ Local-global features + QSVM [32] & 2021 & 69.4 \\ Hybrid Deep Evolving Neural Networks [44] & 2022 & 77.3 \\ ViT + Multi Layer LSTM [45] & 2022 & 96.1 \\
3 DCNN [42] & 2022 & 82.6 \\ DA-R3 DCNN (Proposed) & 2023 & **97.4** \\ \hline \hline \end{tabular} Bold value represents the best accuracy, where the italic value indicates the runner up accuracy.  
\end{table} 
Table 4: Comparative analysis of the proposed DA-R3 DCNN with the state-of-the-art methods on the UCF50 dataset.