# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 4 Results and Discussion
### Run Time Analysis
On the other hand, the Deep autoencoder [35] method exhibited the least favorable performance on CPU resources, achieving an SPF of 0.56 and an FPS of 1.1. These findings further solidify the superior run time performance of the proposed DA-R3 DCNN model when compared to alternative methods on the CPU computing platform. It is evident from the listed scaled and non-scaled results in Table 8 that the proposed DA-R3 DCNN provides significant improvement for both GPU and CPU computing platforms. For instance, for non-scaled run time results, the proposed DA-R3 DCNN provided an improvement of up to 7\(\times\) for SPF and 3\(\times\) for FPS metric when running on GPU resources. When running on CPU resources, the proposed DA-R3 DCNN achieved an improvement of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Seconds per Frame (SPF)**} & \multicolumn{3}{c}{**Frames per Second (FPS)**} \\ \cline{2-6}  & **GPU** & **CPU** & **GPU** & **CPU** \\ \hline \multicolumn{6}{c}{Without Scaling} \\ \hline STPP + LSTM [46] & 0.0053 & - & 2017 & 186.6 & - \\ CNN + Bi-LSTM [8] & 0.0570 & - & 2017 & 20 & - \\ OFF [61] & _0.0048_ & - & 2018 & 215 & - \\ Videolstm [17] & 0.0940 & - & 2018 & 10.6 & - \\ Optical-flow + Multi-layer LSTM [47] & 0.0356 & _0.18_ & 2018 & 30 & 3.5 \\ Deep autoencoder [35] & 0.0430 & 0.43 & 2019 & 24 & 1.5 \\ TSN + TSM [66] & 0.0167 & - & 2019 & 60 & - \\ IP-LSTM [49] & 0.0431 & - & 2019 & 23.2 & - \\ SIDAN [53] & 0.0075 & - & 2020 & 132 & - \\ DS-GRU [39] & 0.0400 & - & 2021 & 25 & - \\ DA-R3 DCNN (Proposed) & **0.0045** & **0.0058** & 2023 & **240** & **187** \\ \hline \multicolumn{6}{c}{With Scaling} \\ \hline STPP + LSTM [46] & _0.0063_ & - & 2017 & 154.6 & - \\ CNN + Bi-LSTM [8] & 0.0974 & - & 2017 & 11.7 & - \\ OFF [61] & 0.0082 & - & 2018 & 125 & - \\ Videolstm [17] & 0.1606 & - & 2018 & 6.2 & - \\ Optical-flow + Multi-layer LSTM [47] & 0.0608 & _0.23_ & 2018 & 17.5 & 2.6 \\ Deep autoencoder [35] & 0.0735 & 0.56 & 2019 & 14 & 1.1 \\ TSN + ISM [66] & 0.0458 & - & 2019 & 21.8 & - \\ IP-LSTM [49] & 0.0736 & - & 2019 & 13.57 & - \\ STDAN [53] & 0.0128 & - & 2020 & 77.2 & - \\ DS-GRU [39] & 0.0683 & - & 2021 & 14.6 & - \\ DA-R3 DCNN (Proposed) & **0.0045** & **0.0058** & 2023 & **240** & **187** \\ \hline \multicolumn{6}{c}{The best and runner-up SPF and FPS scores for GPU and CPU are highlighted in bold and italic text, respectively.} \\ \end{tabular}
\end{table} 
Table 8: Comparison of the run time performance between our proposed DA-R3 DCNN framework and state-of-the-art human action recognition methods, considering both scaled and unscaled results. up to 52\(\times\) for SPF and 74\(\times\) for FPS for the non-scaled run time results. Similarly, for the scaled run time results, the proposed DA-R3 DCNN provided an improvement of 13\(\times\) for SPF and 5\(\times\) for FPS when running on GPU resources. When running on CPU resources, the proposed DA-R3 DCNN framework achieved an improvement of 68\(\times\) for SPF and 100\(\times\) for FPS. These results show the efficiency and applicability of the proposed DA-R3 DCNN method for real-time human activity recognition in resource constraint environments.