## 6 Results and Discussion
By exploring various approaches, researchers aim to develop robust algorithms that can effectively detect physical violence behavior in different scenarios, such as crowd violence, school violence, and aggressive behaviors. Additionally, comparing and evaluating various methods helps identify the strengths and limitations of each approach, leading to the advancement of technology for enhancing security and safety through automated violence detection in surveillance videos. The goal is to create more effective and reliable systems for detecting and responding to physical violence activities, thereby contributing to the overall security and well-being of individuals and communities. Some challenges in accurately detecting violence in surveillance videos include the complexity of identifying features that describe behavior accurately, the impact of scene changes and appearance variations on object behavior analysis, the lack of publicly available datasets for violence detection, data imbalance between positive and negative samples, the computational and time-consuming costs of feature representation during video violence detection, among others [64; 66]. This work analyzes active learning as a tool that could be applied to different models by combining human expert input and an automatic threshold parameter, which also is able to manage diverse scene variations due to the re-training process with human-labeled images during the active learning stage; this was explored by using five well-known and well performing models. Additionally, to our knowledge, no other works have been identified in the field of violence detection in videos that include active learning as a strategy to improve the training process. We discussed the main experimental results obtained by the proposed approach using pre-trained models. Still, clarifying that the presented method could be applied in other neural network models is necessary. We are interested in testing its performance in other advanced deep learning models like transformers in the future. The main goal of this work is only to highlight the effectiveness of the threshold active learning approach to build robust models for violence detection in images obtained from video in diverse \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref** & **Model** & **Metric** & **Value** \\ \hline \multirow{6}{*}{[11]} & \multirow{3}{*}{3D CNN (C3D) + SVM (support vector machine)} & & 97.86–99.6\% \\  & & Accuracy & 0.996–1.0 \\  & & AUC & 96.67–99.05\% \\  & & & 0.9927–0.9994 \\  & ConvLSTM (convolutional LSTM) & & 84.19–96.57\% \\ \hline \multirow{2}{*}{[12]} & ResNet50 + 7 layer FC & \multirow{2}{*}{Accuracy} & 94–100\% \\  & VGG16 + 7 layer FC & & 95.5–100\% \\ \hline \multirow{2}{*}{[14]} & \multirow{2}{*}{4D Convolution} & Accuracy & 94.67–100\% \\  & & F1-score & 0.94–1.00 \\ \hline \multirow{6}{*}{[16]} & DenseNet121 + LSTM (long short-term memory) & \multirow{6}{*}{Accuracy} & 92.05–96.40\% \\  & VGG16 + LSTM & & 79.4–93.3\% \\  & Oriented FAST and Rotated BRIEF (ORB) + VGG16 + LSTM & & 92\% \\  & ORB + DenseNet121 + LSTM & & 94.49\% \\ \hline \multirow{2}{*}{[65]} & C3D + SVM & Accuracy & 95.51–99.29\% \\  & AUC & 0.9832–0.99 \\ \hline \multirow{2}{*}{[66]} & MobileNetV2 + LSTM & Accuracy & 82.0–99.5\% \\ \hline \multirow{3}{*}{This work (final model only)} & DenseNet121 + Active Learning & \multirow{3}{*}{Accuracy} & 0.811–0.881 \\  & EfficientNetB0 + Active Learning & & 0.844–0.964 \\ \cline{1-1}  & MobileNetV2 + Active Learning & & 0.836–0.963 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison to related research; all Refs. worked at the frame level of the video. environments, regardless of which pre-trained model is used or which is the one that attains the best classification performance.