## 5 Experimental Results
### Experiments with CrimeDetection Dataset  
This section evaluates the performance of the proposed model using the CrimeDetection dataset, with a specific focus on analyzing how different video resolutions affect its crime detection capabilities. Various resolution configurations were employed, as detailed in Table 6, beginning with a resolution of 256 \(\times\) 256 pixels and progressively reducing it to 64 \(\times\) 64 pixels. The primary objective of this analysis is to determine whether reducing video resolution significantly impacts the model's accuracy and its ability to distinguish between criminal and non-criminal incidents. Lower resolutions are advantageous for achieving faster processing and reducing computational resource demands. However, a lower resolution may lead to a loss of critical visual information essential for accurate crime detection. To address this, the resolution configurations were carefully selected to achieve a balance between efficiency and accuracy, with the aim of maximizing model performance even under scenarios with limited processing resources. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{Resolution} & \multicolumn{2}{c}{**Training**} & \multicolumn{1}{c}{**Validation**} & \multicolumn{1}{c}{**Test**} & \multirow{2}{*}{**Accuracy**} \\  & & **Samples** & **Samples** & & **Samples** \\ \hline
7 & 256 & 5452 & 663 & 711 & 87.90\% \\ \hline
8 & 128 & 5452 & 663 & 711 & 87.06\% \\ \hline
9 & 64 & 5452 & 663 & 711 & 86.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experiments on CrimeDetection dataset. Figure 8: Confusion matrix training at 64 frames. (**a**) Experiment 1. (**b**) Experiment 4. Based on the results from Experiments 7, 8, and 9, represented in Figure 9, it was observed that the model's ability to discriminate between positive and negative events remained stable despite the progressive reduction in input video resolution. Notably, even with lower-resolution video inputs, the model's performance in terms of AUC and accuracy did not exhibit significant degradation. One of the primary benefits of reducing video resolution is the substantial improvement in training times. Specifically, training times decreased from 8.13 h in Experiment 7 to 2.20 h and 1.87 h in Experiments 8 and 9, respectively. Although training time is not a critical metric for inference performance, it becomes relevant during the initial model setup and future experimental testing phases, as well as when considering system scalability. In this context, the reduction in training time without compromising model accuracy or predictive capability represents a crucial factor for the implementation of the model in a real-time crime detection environment. The outcomes of these experiments, presented in Figure 10, indicate satisfactory model performance in the classification task. The model's predictions closely align with the true classes, demonstrating its ability to effectively differentiate between criminal incidents and non-criminal situations. The confusion matrices for Experiments 7, 8, and 9 (Figure 10) reveal consistent results, with strong model performance reflected in a high degree of alignment between predicted and actual class labels. The confusion matrices indicate a high level of precision in detecting criminal events, with relatively few false positives and false negatives. Furthermore, the AUC-ROC values obtained in these experiments range from 94.21% to 95.28%, further reinforcing the model's ability to accurately distinguish between criminal and non-criminal incidents. This AUC-ROC range, approaching the ideal 100%, suggests an excellent capacity for class discrimination. This level of performance is particularly  
Figure 10: Confusion matrices for Experiments 7 (**a**), 8 (**b**), and 9 (**c**) using CrimeDetection. Figure 9: ROC-AUC curves for CrimeDetection in Experiments 7 (**a**), 8 (**b**), and 9 (**c**). important to ensure the model's reliability for use in security applications.