# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 5 Conclusions and Future Research Directions  
In this work, we have proposed an attention-driven 3 DCNN with residual skip connections for recognizing human activities in videos. The proposed method combines the powerful characteristics of dual channel-spatial attention and residual 3D convolutional neural network (3 DCNN) into a unified framework for efficient modeling of human actions and single instance training. The utilized dual channel-spatial attention mechanism incorporates both channel and spatial attentions, enabling the extraction of highly discriminative features from regions of interest related to the objects involved in the activities. This results in the generation of high-quality feature maps containing object saliency-aware features boosting the overall learning process of the proposed residual 3 DCNN network. By employing residual 3 DCNN coupled with dual attention, our method, known as DA-R3 DCNN, effectively captures the temporal dynamics of human actions through the use of multiple 3D kernels. By leveraging the knowledge acquired from the immediately preceding frames within the input sequence, the model becomes capable of learning the spatial and temporal relationships within unseen frames. This enables the model to grasp the connections and patterns existing between different frames. The incorporation of attention-guided learning further enhances our method's capability to acquire spatial and temporal understanding of human actions, leading to improved learning performance during training and enhanced prediction accuracy during inference. We have extensively evaluated the performance of our proposed DA-R3 DCNN method on four widely recognized benchmark datasets for human action recognition: UCF11, UCF50, HMDB51, and UCF101. These datasets are well-established in the research community and serve as reliable benchmarks for comparison. Through rigorous experimentation and comparison with the state-of-the-art approaches, we have demonstrated the superiority of our method in terms of model robustness and computational efficiency. The obtained results validate the efficacy of our approach in tackling the challenges of human action recognition across diverse datasets. Further, we have assessed the run time performance of our proposed framework in terms of seconds per frame (SPF) and frames per second (FPS) on both CPU and GPU execution environments. This analysis has allowed us to measure the computational efficiency of our method, and to provide valuable insights into the speed of frame processing and overall video processing capabilities across different hardware configurations. The run time assessment results clearly indicate that the proposed DA-R3 DCNN method exhibits remarkable improvements when leveraging GPU resources. It demonstrates a significant enhancement of up to 13\(\times\) in SPF and 5\(\times\) in FPS metrics as compared to the state-of-the-art methods. Additionally, even when limited to CPU resources, our approach achieves substantial advancements, with SPF improving by 68\(\times\) and FPS by 100\(\times\) as compared to existing approaches. These findings establish that the proposed DA-R3 DCNN method is exceptionally well-suited for real-time human activity recognition on resource-constrained devices. While our current implementation of the DA-R3 DCNN method leverages the spatial attention mechanism (channel and spatial attention), which has proven to be highly effective, in our future work, we plan to incorporate a temporal attention mechanism for precise temporal localization of human activities within video scenes. Additionally, we are actively exploring the integration of multi-modal data, which holds significant potential for recognizing complex human activities in uncertain environments. These future advancements aim to further enhance the capabilities of our method in capturing both spatial and temporal dynamics for improved activity recognition performance. **Author Contributions:** Conceptualization, H.U. and A.M.; methodology, H.U. and A.M.; software, H.U.