## 4 Experiments and Evaluation
### Failure Cases and the Potential for Future Research  
In this section, we address the failure cases of the proposed DeepGun approach and provide a discussion on directions for future research. A primary objective in any firearm detection system is to sustain a high detection rate while minimizing both FPs and FNs during testing. However, maintaining a high detection rate by merely reducing the FP rate can result in an unacceptably high number of false alarms when the system is deployed in  
\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{8}{*}{_IFC_} & \(F_{1}\) & 83 \(\pm\) 0.00 & 82 \(\pm\) 0.01 & 83 \(\pm\) 0.01 & 82 \(\pm\) 0.02 & 85 \(\pm\) 0.01 & 83 \  
real-world surveillance scenarios. The failure cases observed in the VISILAB dataset are illustrated in Figure 10.  
In Figure 10a, the subject is either carrying objects other than a handgun or has empty hands. In such case, partial occlusion of the hands or the extraction of background patches instead of meaningful hand regions can lead to unreliable features, which are a common cause of FPs. To mitigate this issue, we propose integrating an FP filtering mechanism that preserves the handgun detection capability of the system, as suggested in [61]. This filtering step may involve training a specialized model on the false positive detections collected over time while deploying the firearm detector in the target environment. In another example, illustrated in Figure 10b, the subject is approaching the camera while carrying a handgun. However, due to poor lighting conditions and the firearm being inadequately depicted, the patch extracted from the hand does not yield robust features. In such cases, distorted firearm characteristics and ambiguous pose information can result in an unreliable unified feature vector, often leading to false negatives (FNs). To address this challenge, we propose utilizing the approach introduced in [62], which can extracts multiple ROI (multi-ROI) from the hands to construct a fused feature vector. Additionally, a more robust VAE, as described in [63], could be employed to extract more discriminative features from the hand patches. In our approach, accurately extracting the hand positions for both hands from the skeleton is crucial to ensure that the hand ROI is correctly cropped, enabling reliable feature extraction by the VAE. Moreover, body landmarks are vital for detecting dangerous poses. However, MediaPipe often fails to provide consistent results. In some videos, landmarks are successfully detected in one frame but entirely missing in the subsequent frame, even though the frames are visually similar and the subject is clearly visible. An example of this failure case is illustrated in Figure 10c, where body landmarks could not be extracted using the default settings, despite the subject being clearly captured by the imaging device. Figure 10: Sample frames from the VISILAB dataset illustrating failure cases: (**a**) false positive (FP); (**b**) false negative (FN); and (**c**) failure of MediaPipe Holistic to detect any body landmarks (MediaPipe Failure). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **DeepGun-CNN** & **DeepGun-E2** & **DeepGun-FPS** & **DeepGun** \\ \hline \(F_{1}\) & 83.7 & 85.9 & 81.3 & **86.6** \\ \(ACC\) & 82.6 & 84.3 & 79.3 & **85.2** \\ \(P\) & 88.8 & 94.1 & 87.2 & **95.3** \\ R & 74.0 & 73.6 & 68.6 & **74.0** \\ \(AP\) & 78.7 & 83.0 & 75.5 & **83.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation study. For simplicity, we use the following acronyms to denote different versions of proposed DeepGun approach for ablation study: DeepGun-E2 (DeepGun with 2 layers in encoder for feature extraction), DeepGun-CNN (DeepGun with CNN architecture), and DeepGun-FPS (DeepGun with fixed patch size). We attempted to adjust various confidence thresholds, but these efforts did not resolve the issue.