## 4 Experiments and Evaluation
### Comparative Analysis with Other Firearm Detection and Action Recognition Frameworks  
To evaluate the performance of our firearm detection model, we chose accuracy (ACC) as the primary evaluation metric for comparative analysis. The performance of DeepGun was assessed in comparison to R-CNN [16], TSGD [18], Video-FocalNets [20], Movinets [19], and 3D-CNNs [21] using the VISILAB and UCF-Crime firearm datasets, as presented in  
Figure 9: The ROC curves for DeepGun implemented with IFC, SVM, GMM, and MCDE.  
Table 2. Furthermore, we compare our DeepGun approach with the recently proposed data-driven anomaly detection method for images including VAE-SIMCA [43], and TSGD [18]. To compare, we selected ACC, precision (P), and specificity (S) as evaluation metrics for a more comprehensive performance assessment (see Table 3).  
From Table 2, we observe that the DeepGun method outperforms other firearm detection and action recognition approaches in terms of ACC. The performance of Video-FocalNets and MoViNets is comparable to that of DeepGun. Furthermore, Table 3 indicates that when comparing our VAE-based feature extraction approach with VAE-SIMCA, which is a reconstruction error-based OCC anomaly detection method, DeepGun demonstrates superior performance in terms of ACC, precision (P), and specificity (S). However, TSGD achieves higher precision compared to both DeepGun and VAE-SIMCA. Overall, the proposed DeepGun method, leveraging a combination of body landmarks and visual gun features, proves to be highly effective for firearm detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **ACC** & **ACC** \\  & **(VISILAB)** & **(UCF-Firearm)** & **Model Specifications** \\ \hline \multirow{3}{*}{DeepGun} & \multirow{3}{*}{85.2} & \multirow{3}{*}{84.1} & VAE with KL loss and reconstruction loss, NN encoder with 4 blocks, 1D kernel, latent space 64,200 epochs, early stopping with patience 10, input patch size 32 \(\times\) 32. \\ \hline \multirow{3}{*}{R-CNN [16]} & \multirow{3}{*}{82.2} & \multirow{3}{*}{80.5} & VGG-16 pretrained on Imagenet database, mini-batch gradient descent with momentum, 50 epochs, early stopping with patience 10, input size 224 \(\times\) 224. \\ \hline \multirow{3}{*}{TSGD [18]} & \multirow{3}{*}{51.9} & \multirow{3}{*}{67.16} & YOLOv11 for gun detection, VGG trained on Imagenet and Transformer [60] combination for handling the temporal features in videos, 200 epochs, early stopping, input frame size 640 \(\times\) 640. \\ \hline \multirow{3}{*}{Video-FocalNets [20]} & \multirow{3}{*}{83.33} & \multirow{3}{*}{75} & Spatio-temporal focal modulation, 4 blocks with 2D depthwise followed by 1D pointwise convolution, 120 epochs, batch size 512. \\ \hline \multirow{3}{*}{Movinets [19]} & \multirow{3}{*}{83.44} & \multirow{3}{*}{79.16} & Vision transformer, 5 blocks with 3D-CNN kernel, 240 epochs, batch size 1024. \\ \hline \multirow{3}{*}{3D-CNNs [21]} & \multirow{3}{*}{67.39} & \multirow{3}{*}{67.16} & Spatiotemporal 3D CNNs with 34 layers, 45 epochs, input size \(128\times 171\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of firearm detection accuracy (ACC) from VISILAB and UCF-Crime benchmark firearm dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **ACC** & **Precision (P)** & **Specificity** \\ \hline DeepGun & 85.2 & 95.3 & 96.4 \\ \hline VAE-SIMCA [43] & 62.0 & 93.8 & 43.0 \\ \hline TSGD [18] & 51.9 & 98.1 & 50.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comprehensive analysis of DeepGun, TSGD, and VAE-SIMCA on VISILAB firearm action recognition dataset.