## 3 Experimental Results and Analysis
### Performance Comparison with Related Approaches  
We also compared the performance of the proposed action recognition framework with two existing approaches, as reported in the benchmark paper [22] (see Table 5). The  
Figure 7: Confusion matrix for Split 2.  
benchmark paper provides an analysis of the classification accuracy of two methods, including the high-level pose features (HLPFs) method and the pose-based convolutional neural networks (P-CNNs) method. The high-level pose features (HLPFs) method uses skeletal information from human poses to represent actions. In P-CNN, at each frame of a video, descriptors are extracted from the body regions. These descriptors encode relevant information, such as motion flow patterns and visual characteristics of the regions, leading to two-streamed information. Over time, these descriptors are aggregated, combining the information from multiple frames, to form a video descriptor. The proposed method shows better or comparable performance as compared to these existing methods (Table 5), owing to its capability to efficiently model temporal information and long-term dependencies in action sequences. For a more detailed performance comparison of the proposed approach with other models, we investigated several state-of-the-art deep learning models, such as Action Transformer, ResNet18, ResNet101, 3D ResNet, and ST-GCN. Action Transformer [33] has recently been employed for human action recognition. For evaluation, we set the  
Figure 8: Confusion matrix for Split 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Accuracy** & **Accuracy** \\
**(Split 1)** & **(Split 2)** & **(Split 3)** \\ \hline HLPF & 63.89\% & 68.09\% & 61.11\% & 64.36\% \\ P-CNN & 72.22\% & 81.94\% & 73.61\% & 75.92\% \\ Pose+ LSTM & 74.00\% & 80.00\% & 70.00\% & 74.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the proposed method with existing approaches on Drone Action dataset. corresponding parameters as follows, heads: 1, layers: 4, embedding dimensions: 64, MLP: 256, and encoder layers: 5. The reason to keep the parameters at a minimum is to reduce the computational complexity of the model for the application at hand. ResNet is a specific configuration of the architecture that consists of 101 layers in the case of ResNet101 and 18 layers in ResNet18. The network includes residual blocks, which are designed to learn residual mappings that help mitigate the vanishing gradient issue. Each residual block contains multiple convolutional layers and shortcut connections that allow information to flow more effectively through the network. ResNet networks have been widely used for several computer vision tasks [34]. Further, 3D ResNet is an extension of the ResNet architecture designed to tackle video action recognition tasks by considering both spatial and temporal features in videos [28]. It adds a temporal dimension to the standard ResNet architecture, making it well suited for analyzing sequences of frames in videos. Thus, 3D ResNet takes advantage of this temporal aspect by incorporating 3D convolutional layers. These layers consider the spatial relationships within each frame as well as the temporal relationships between consecutive frames, enabling the network to capture motion patterns and changes over time. Finally, the Spatio-Temporal Graph Convolutional Network (ST-GCN) [35] is also a useful architecture used in video action detection applications, especially for addressing the spatial and temporal features present in films. In order to capture both spatial correlations within individual frames and temporal dependencies between successive frames, ST-GCN uses graph convolutional procedures. For evaluation, we replaced the proposed ConvLSTM with each of the above-mentioned models and accordingly trained and tested them on the same lines for all the three splits of the dataset. Figure 9 presents the performance comparison of the proposed approach with these models in terms of the mean accuracy across the three splits.