## 4 Experiments and Evaluation
### DeepGun vs. Deep Architectures as Feature Extractor  
Although our proposed two-step approach primarily relies on VAE for visual gun feature extraction, we also performed a comparative evaluation against other deep learning architectures. The core idea behind this evaluation is to utilize pre-trained networks on the ImageNet database for feature extraction from hand patches captured by the imaging device. These pre-trained models are designed to learn rich feature representations from large-scale image datasets, making them highly effective for extracting meaningful features from new images. By utilizing these pre-trained networks, we aim to convert hand patches into a set of relevant and discriminative features. These features serve as a compact yet informative representation of the hands, enabling the classification of images into two categories: hands holding a firearm and hands without a firearm. During the inference phase, these extracted features combined with the body landmarks play a crucial role in distinguishing between firearm and non-firearm scenarios, enhancing the overall accuracy and robustness of the detection system. For a comprehensive analysis, we replaced our VAE-based feature extraction with five alternative deep architectures--ResNet50 [55], VGG19 [56], NasNetLarge [57], DenseNet201 [58], and MobileNetV2 [59]. On the VISILAB firearm dataset, our VAE-based feature extraction and IFC-based OCC model outperforms these six architectures, demonstrating superior performance in terms of F1 score, ACC, P, R, and AP. The results, presented in Table 4, provide a comprehensive comparative evaluation of firearm detection across both _'handgun'_ and _'no gun'_ samples. Notably, replacing simple AE [48] with VAE for feature extraction significantly improves firearm detection performance, as shown in the second-last column of Table 4, when implemented with different state-of-the-art OCC methods, including IFC, SVM, and MCDE, except GMM. While AE and GMM-based implementations exhibit some performance improvements, they lag behind other feature extraction-based firearm detection approaches. The experimental results show that the proposed OCC-based firearm detection \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Metric** & **Nas-NetLarge** & **Dense-Net201** & **VGG19** & **Mobile-NetV2** & **Res-Net50** & **AE** & **DeepGun** \\ \hline \multirow{4}{*}{_IFC_} & \(F_{1}\) & 85.1 & 82.3 & 83.7 & 83.2 & 83.7 & 81.3 & **86.6** \\  & \(ACC\) & 83.4 & 80.9 & 82.2 & 81.9 & 82.6 & 79.3 & **85.2** \\  & \(P\) & 90.1 & 87.2 & 89.8 & 88.2 & 87.7 & 87.2 & **95.3** \\  & R & 75.0 & 72.4 & 72.6 & 73.6 & **75.8** & 68.6 & 74.0 \\  & \(AP\) & 80.1 & 76.9 & 78.9 & 78.1 & 78.6 & 75.5 & **83.5** \\ \hline \multirow{4}{*}{_SVM_} & \(F_{1}\) & 62.1 & 68.6 & 69.4 & 66.0 & 64.8 & 53.5 & **79.2** \\  & \(ACC\) & 70.6 & 74.3 & 75.1 & 72.7 & 72.5 & 66.3 & **81.1** \\  & \(P\) & 64.2 & 67.8 & 68.3 & 66.3 & 65.6 & 60.5 & **76.3** \\  & R & 93.0 & 92.2 & 93.6 & 92.2 & 94.2 & **94.8** & 90.2 \\  & \(AP\) & 63.2 & 66.4 & 67.1 & 65.0 & 64.7 & 59.8 & **73.7** \\ \hline \multirow{4}{*}{_MCDE_} & \(F_{1}\) & 61.0 & 61.0 & 61.1 & 61.2 & 62.3 & 61.0 & **77.7** \\  & \(ACC\) & 51.6 & 51.6 & 51.7 & 51.8 & 54.5 & 51.7 & **79.1** \\  & \(P\) & 51.0 & 51.0 & 51.1 & 51.2 & 53.1 & 51.1 & **83.4** \\  & R & 76.0 & 75.8 & 76.0 & **76.2** & 75.4 & 75.8 & 72.8 \\  & \(AP\) & 50.8 & 50.8 & 50.8 & 50.9 & 52.3 & 50.8 & **74.3** \\ \hline \multirow{4}{*}{_GMM_} & \(F_{1}\) & 41.0 & 49.0 & 57.2 & 53.1 & 44.7 & **68.3** & 56.8 \\  & \(ACC\) & 47.8 & 49.4 & 52.3 & 52.0 & 47.8 & **65.7** & 52.0 \\ \cline{1-1}  & \(P\) & 47.1 & 49.3 & 51.8 & 51.9 & 47.2 & **63.4** & 51.6 \\ \cline{1-1}  & \(R\) & 36.4 & 48.8 & 63.8 & 54.4 & 42.6 & **74.2** & 63.3 \\ \cline{1-1}  & \(AP\) & 48.9 & 49.7 & 51.1 & 51.0 & 48.8 & **59.9** & 51.0 \\ \hline \hline \end{tabular}
\end{table}