## 6 Results and Discussion
Still, it is limited when the scenarios are very different (see Figure 2). However, the advantage of the proposed model is that it continuously learns from the environment, and the classifier will adapt in future iterations (see Algorithm 1) to improve its performance. The performance on the SCVD dataset remains consistent with that obtained by the final model, as in this phase the initial and final models are equivalent. Another advantage of our proposed approach is that it is only retrained with samples that are hard to learn for the classifier, and those samples are confirmed by a human expert. The experimental results presented in this section show that the proposed approach's performance is appropriate for working in changing environments. The key is the classifier's ability to retain the knowledge learned while it learns new knowledge verified by a human expert. In other words, the main idea is to maintain an acceptable classification performance on the previous images learned while new images are learned from unknown environments. The additional effort regarding human expert hours associated with the proposed threshold active learning (see Section 4) is justified. The model cannot remain static because the nature of the environment is changeable: new videos in different scenarios are generated daily. Consequently, the classifier should be rebuilt to adapt to new scenarios (which is a challenge for static classifiers). For this, new high-quality datasets are needed; thus, the contribution of human experts in this process is of great importance because, unlike static classifiers, the proposed approach only uses those samples where the classifier has uncertainty about their correct label. State of the Art: The Best-Performing Techniques  
The state-of-the-art best-performing techniques for violence detection in surveillance videos include two main approaches: sequence and frame levels. Oriented Violent Flows (OViF), SVM classifiers, and spatiotemporal autocorrelation of gradients (STACOG) for feature extraction are some relevant representatives of the first approach. Deep learning methods such as CNN, LSTM, and 3D ConvNets have shown promising results in violence detection at a frame-level analysis [64]. In Table 7, we present the results of similar works that employed various models at the frame level to detect violence in videos. These works consistently report good performance. However, our research emphasizes the use of active learning, which, while not yielding substantially superior results compared to other authors, does enhance the performance compared to those without active learning. Specifically, our approach with active learning improves the pre-trained models' performance in nearly all cases (see Table 6). Furthermore, it is important to highlight that no modifications or adaptations to the model architecture were necessary. This indicates the potential of active learning to optimize model efficiency and performance, even within established frameworks, and distinguishes our approach since we have not identified references where active learning is part of the method. \begin{table}
\begin{tabular}{l l c c c} \hline \hline  & \multicolumn{3}{c}{**AUC**} & \\  & **Model** & **AIRTLab** & **RVLS/Pexels** & **SCVD** \\ \hline DenseNet121 & Initial model & 0.907 & 0.961 & 0.811 \\  & Final model & 0.895 & 0.881 & 0.811 \\ EfficientNetB0 & Initial model & 0.907 & 0.982 & 0.914 \\  & Final model & 0.964 & 0.844 & 0.914 \\ MobileNetV2 & Initial model & 0.939 & 0.989 & 0.903 \\  & Final model & 0.963 & 0.836 & 0.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the initial and final models’ performance (AUC metric) with respect to the original dataset test of AIRTLab, RVLS/Pexels, and SCVD. The purpose of conducting research using different techniques and models for violence detection in videos is to improve surveillance systems' accuracy, efficiency, and reliability in identifying and preventing physical violence incidents.