# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 4 Results and Discussion
### Comparison with the State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Year** & **Accuracy (\%)** \\ \hline Multi-task hierarchical clustering [33] & 2016 & 76.3 \\ Saliency-aware 3 DCNN with LSTM [59] & 2016 & 84.0 \\ Spatio-temporal multilayer networks [60] & 2017 & 87.0 \\ Long-term temporal convolutions [20] & 2017 & 82.4 \\ CNN + Bi-LSTM [8] & 2017 & 92.8 \\ OFF [61] & 2018 & 96.0 \\ TVNet [62] & 2018 & 95.4 \\ Attention cluster [63] & 2018 & 94.6 \\ Videolstm [17] & 2018 & 89.2 \\ Two stream convnets [64] & 2018 & 84.9 \\ Mixed 3D-2D convolutional tube [65] & 2018 & 88.9 \\ TS-LSTM + Temporal-inception [50] & 2019 & 91.1 \\ TSN + TSM [66] & 2019 & 94.3 \\ STM [67] & 2019 & 96.2 \\ Correlational CNN + LSTM [52] & 2020 & 92.8 \\ ResCNN-DBLSTM [68] & 2020 & 94.7 \\ SC-BDLSTM [69] & 2021 & 94.2 \\ Ensemble model with swarm-based optimization [43] & 2021 & 96.3 \\ BS-2 SCN [41] & 2022 & 90.1 \\ TDS-BiLSTM [70] & 2022 & 94.7 \\ META-RGB+Flow [71] & 2022 & 96.0 \\ Spurious-3D Residual Network [72] & 2023 & 95.6 \\ DA-R3 DCNN (Proposed) & 2023 & **97.8** \\ \hline \hline \end{tabular} Bold value represents the best accuracy, where the italic value indicates the runner up accuracy.  
\end{table} 
Table 6: Comparative analysis of the proposed DA-R3 DCNN with state-of-the-art methods on the UCF101 dataset. from the results listed in Table 6 that the proposed DA-R3 DCNN exhibited an average accuracy improvement of 7.17% as compared to the average results of state-of-the-art methods on the UCF101 dataset. Additionally, for clear understanding of comparative assessment, we also present the visual overview of comparative analysis of our proposed DA-R3 DCNN with the state-of-the-art human action recognition methods on UCF11, UCF50, HMDB51, and UCF101 datasets in Figure 6. The quantitative comparisons depicted in Figure 6 illustrate the performance of comparative methods published up to 2023. Furthermore, to assess the performance generalization of our method, we conducted an analysis of confidence intervals, following the methodology outlined in [73]. This analysis was performed on each dataset used in this study, and a comparison was made between the confidence intervals of our proposed method and those of the state-of-the-art approaches. It is worth noting that a confidence level of 95% was employed for estimating the confidence intervals of both our method and the state-of-the-art methods. The resulting confidence interval values for our proposed method and the state-of-the-art methods are given in Table 7. Upon examining these values, we observe that our proposed method exhibited higher confidence levels with narrower intervals on all the dataset when compared to the state-of-the-art methods. For instance, for the UCF11 dataset, the confidence interval of our proposed method spanned from 97.31 to 99.10, with a range of only 1.79. In contrast, the average confidence interval of the state-of-the-art methods ranged from 87.74 to 94.20, showing a comparatively larger range of 6.46. Similarly, for the UCF50 dataset, our proposed method achieved a confidence interval between 96.78 and 98.42, with a small range of 1.64, while the state-of-the-art methods had an average confidence interval ranging from 79.86 to 95.74, indicating a larger range of 15.88. Analyzing the HMDB51 dataset, we observe that our proposed method had a confidence interval of 92.21 to 94.16, with a narrow range of 1.95. In contrast, the state-of-the-art methods exhibited an average confidence interval ranging from 65.97 to 72.67, demonstrating a comparatively larger range of 6.70. Lastly, for the UCF101 dataset, our proposed method demonstrated a confidence interval between 96.89 and 98.46, with a small range of 1.57, whereas the state-of-the-art methods have an average confidence interval ranging from 88.93 to 93.57, indicating a larger range of 4.64. It is worth mentioning here that our proposed method consistently achieved higher confidence levels across all datasets, with narrower intervals, in comparison to the state-of-the-art methods.