# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 4 Results and Discussion
### Run Time Analysis  
In this section, we examine the inference time of our proposed DA-R3 DCNN framework and assess its suitability for real-time human activity recognition tasks, considering metrics such as SPF and FPS. To evaluate the overall run time performance, we conducted inference time measurements of the proposed DA-R3 DCNN model on both GPU and CPU computing platforms. These measurements were then compared with the runtime results of the state-of-the-art human activity recognition methods, and the findings are presented in Table 8. This analysis provides a comprehensive perspective on the run time efficiency of our proposed DA-R3 DCNN model across different computing platforms. The results presented in Table 8 highlight the superior inference efficiency of the proposed DA-R3 DCNN model as compared to state-of-the-art methods, as demonstrated by SPF and FPS metrics on both GPU and CPU computing platforms. The findings indicate that, when utilizing GPU resources, our proposed DA-R3 DCNN achieved the best SPF of 0.0045 and an FPS of 240. The runner-up method, OFF [61], achieved an SPF of 0.0048 and an FPS of 215. Conversely, the Videolstm [17] method exhibited the highest SPF of 0.0940 and the lowest FPS of 10.6, indicating the least favorable run time performance among all the comparative methods. These results underscore the exceptional inference efficiency of our proposed DA-R3 DCNN model when compared to existing approaches. On the CPU computing platform, the proposed DA-R3 DCNN framework demonstrated significant superiority over existing methods, achieving an SPF of 0.0058 and an FPS of 187. In comparison, the second-best performing method, Optical-flow + Multi-layer LSTM [47], achieved an SPF of 0.18 and an FPS of 3.5. Conversely, the Deep autoencoder [35] method exhibited the poorest runtime performance with an SPF of 0.43 and an FPS of 1.5. These results further validate the exceptional run time efficiency of our proposed DA-R3 DCNN framework when compared to alternative approaches on the CPU computing platform. Further, to ensure a fair comparison of the run time results obtained for both GPU and CPU platforms, we scaled the run time results (as in [74]) of the state-of-the-art methods to match the hardware resources utilized in our study (i.e., a 2.5 GHz CPU and a  
Figure 6: The graphical overview of the conducted comparative analysis of our proposed DA-R3 DCNN with the state-of-the-art methods on (**a**) UCF11 dataset, (**b**) UCF50 dataset, (**c**) HMDB51 dataset, and (**d**) UCF101 dataset. 585 MHz GPU). The scaled run time results are provided in the second section of Table 8, enabling an equitable assessment and comparison of the performance of the proposed DA-R3 DCNN framework against existing methods. Analyzing the scaled results presented in Table 8, it becomes evident that scaling amplifies the advantages of the proposed DA-R3 DCNN model in terms of SPF and FPS metrics for both GPU and CPU computing platforms. When utilizing GPU resources, the proposed DA-R3 DCNN outperformed other methods with the best SPF of 0.0045 and an FPS of 240. The STPP + LSTM [46] method secured the second-best position, with SPF and FPS values of 0.0063 and 154.6, respectively. These findings highlight the enhanced performance of the proposed DA-R3 DCNN model when considering the scaled runtime results, solidifying its superiority over alternative approaches. The Videolstm [17] method had the highest SPF of 0.1606 and lowest FPS of 6.2, indicating the worst run time results amongst all the comparative methods. When running on CPU computing platform, the proposed DA-R3 DCNN framework had the lowest SPF of 0.0058 and highest FPS of 187, indicating the best results obtained on CPU resources as compared to other comparative methods. Among the scaled results in Table 8, the Optical-flow + Multi-layer LSTM [47] emerged as the runner-up with an SPF of 0.23 and an FPS of 2.6 on the CPU computing platform.