## 5 Experimental Results
### Experiments with RWF2000 and Real-Life Violence Situations (RLVS) Datasets  
In this initial phase, the objective was to evaluate how sample length influences the model's performance. Multiple shorter samples were generated from the original videos, each of which had an approximate length of 125 frames. In the first experiment, 64-frame samples were extracted with a sampling frequency of every two frames, maintaining a balance between the volume of data and the information contained in each sample. Subsequently, additional experiments were conducted by dividing the samples into 20-frame and 10-frame segments. While this strategy increased the number of available instances for training, it also resulted in a reduction in the amount of information contained in each sample. The experiments are organized according to these configurations and are presented in Table 5.  
In general, all experiments conducted during this stage exhibited a similar training pattern, as illustrated in Figure 5, corresponding to Experiment 5. The figure shows how the model's accuracy steadily increased during the initial epochs of training, reaching values close to 98%. However, validation accuracy exhibited some fluctuations before eventually stabilizing at a value lower than that observed in the training set. This behavior is typical in deep neural networks when facing overfitting issues, where the model performs well on training data but struggles to generalize effectively on unseen validation data. The objective at this stage was to evaluate the influence of sample length on event detection to identify an efficient configuration that enables reduced reaction times--an essential factor for potential implementation in real-time detection applications. The results indicated that as sample length decreased, the model's ability to accurately discriminate events gradually diminished, as depicted in Figures 6 and 7. Consequently, a sample length of 64 frames was established for subsequent experiments to strike a balance between model accuracy and feasibility in real-time scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\#** & **Dataset** & **Frames** & \begin{tabular}{c} **Training** \\ **Samples** \\ \end{tabular} & \
\begin{tabular}{c} **Validation** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Test** \\ **Samples** \\ \end{tabular} \\ \hline
1 & RWF2000 & 64 & 1600 & 200 & 200 \\ \hline
2 & RWF2000 & 20 & 4800 & 600 & 600 \\ \hline
3 & RWF2000 & 10 & 9600 & 1200 & 1200 \\ \hline
4 & RLVS & 64 & 862 & 112 & 110 \\ \hline
5 & RLVS & 20 & 4319 & 555 & 547 \\ \hline
6 & RLVS & 10 & 8914 & 1142 & 1130 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RWF2000 and RLVS experiments at 256-pixel resolution. To gain a more in-depth understanding of the results obtained with 64-frame samples in Experiments 1 and 4--yielding AUC values of 90.77% and 99.29%, respectively--the corresponding confusion matrices are presented in Figure 8. These figures demonstrate that the model achieved a certain level of precision in distinguishing between criminal and normal events. Figure 5: Experiment datasets RWF2000 and RLVS with varying sample length. Figure 6: ROC-AUC curves at 64 frames. (**a**) Experiment 1. (**b**) Experiment 4. Figure 7: ROC-AUC curve at 10 frames. (**a**) Experiment 3. (**b**) Experiment 6. In the confusion matrix for Experiment 1, the model demonstrated a balanced performance, correctly classifying 79 positive events and 84 negative events. However, 16 false positives and 21 false negatives were also observed. In the confusion matrix for Experiment 4, the model correctly identified 69 positive (criminal) events and 31 negative (normal) events. Despite this, nine false positives and one false negative occurred, indicating that while the model exhibited good accuracy, classification errors persisted, suggesting that the model faced challenges in efficiently discriminating between both classes.