## 3 Experimental Results and Analysis
### Evaluation of Results  
The proposed action recognition framework for aerial videos demonstrates an improved accuracy and robustness. Indeed, the combination of the YOLOv8-Pose algorithm and customized sequential convolutional LSTM model effectively captures the spatial and temporal information of actions, leading to an encouraging action recognition performance. The proposed model is trained and tested separately on the three dataset splits, as provided by the original paper [22]. In each split, 70% data are used for training and 30% for testing. The training was carried out for 200 epochs (chosen empirically), and network parameters were kept the same for training and testing for each split of the data. Table 1 lists the corresponding values for the validation loss and accuracy on all three splits. A representative graphical representation of the validation loss and validation accuracy is shown in Figure 5 for Split 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Epochs** & **Validation Loss** & **Validation Accuracy** \\ \hline Split 1 & 200 & 2.75–0.25 & 0.05–0.88 \\ Split 2 & 200 & 2.55–1.00 & 0.13–0.83 \\ Split 3 & 200 & 2.58–1.00 & 0.12–0.82 \\ \hline \end{tabular}
\end{table} 
Table 1: Training details for the 3 splits. Figure 4: Representative frames from each class of the Drone Action dataset [22]: (**a**) walking front_back, (**b**) walking side, (**c**) jogging front_back, (**d**) jogging side, (**e**) running front_back, (**f**) running side, (**g**) hitting with bottle, (**h**) hitting with stick, (**i**) stabbing, (**j**) punching, (**k**) kicking, (**l**) clapping, (**m**) waving hands. The overall accuracies achieved on Split 1, Split 2, and Split 3 are 74%, 80%, and 70%, respectively. The corresponding confusion matrices are provided in Figures 6-8, respectively. The class-wise results on each split are given in Tables 2-4, respectively, based on the standard well-known evaluation measures, which are precision, recall, and F1-score. Analyzing the results in more detail, we observe that some actions had consistently high precision, recall, and F1-score values across all dataset splits. For instance, the actions \"Clap\", \"Kick\", \"Walk_fb\", \"Walk_side\", and \"Wave_hands\" achieved high scores on all three splits. This suggests that the proposed framework is highly effective in recognizing these actions, even when presented with variations in the data. The high accuracy in these classes can be attributed to the combination of YOLO-Pose and the custom-designed ConvLSTM network, which allows for an efficient extraction of spatial and temporal information in video frames. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action** & **Precision** & **Recall** & **F1-Score** \\ \hline Clap & 1.00 & 1.00 & 1.00 \\ Hit\_bdl & 0.19 & 0.14 & 0.16 \\ Hit\_stick & 0.65 & 0.64 & 0.65 \\ Jogging & 0.73 & 0.88 & 0.80 \\ Jog\_side & 0.91 & 0.89 & 0.90 \\ Kick & 0.99 & 1.00 & 0.99 \\ Punch & 0.91 & 0.99 & 0.95 \\ Run\_fb & 0.50 & 0.40 & 0.44 \\ Run\_side & 0.86 & 0.89 & 0.87 \\ Stab & 0.29 & 0.40 & 0.34 \\ Walk\_fb & 1.00 & 0.90 & 0.95 \\ Walk\_side & 1.00 & 1.00 & 1.00 \\ Wave\_hands & 0.98 & 1.00 & 0.99 \\
**Average** & **0.77** & **0.78** & **0.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of the proposed method on all action types based on precision, recall, and F1-score on Split 1. Figure 5: Plots for the validation accuracy (**left**) and validation loss (**right**) during the training for Split 1.