## 5 Materials and Methods  
### Data Sources  
Utilizing three distinct publicly available datasets enriches the diversity and realism of the training and evaluation process in our work. By incorporating diverse datasets, our study aims to train and evaluate the physical violence detection model across various scenarios, enhancing its adaptability and robustness in real-world applications. The datasets are:  
1. AIRTLab dataset: This dataset presents scenes depicting everyday situations that may be misinterpreted by the algorithms as violent actions, such as hugs, clapping, and greetings, performed by nonprofessional actors. Including such scenarios challenges the classifier to discern subtle differences between benign and aggressive behaviors [57]. 2. Real-Life Violence Situations (RVLS) dataset: This dataset comprises images extracted from videos depicting real-life situations and provides a comprehensive collection of diverse scenarios encountered in various environments [58]. By focusing specifically on instances of violence, this dataset facilitates the classifier's training in identifying and classifying violent behaviors accurately. The images in this dataset have been carefully selected and captured from multiple perspectives, incorporating a diversity of angles and different contexts or backgrounds to ensure wide variability and improve the effectiveness of recognition and analysis algorithms. 3. Pexels [59] is an online platform renowned for its vast collection of free stock photos and videos. The Pexels dataset was obtained by carefully selecting images from this platform in the context of this study. These images were specifically chosen to represent real-life scenes that do not contain physical violence, thus complementing the RVLS dataset, which focuses on violence images. Including Pexels images in the study is essential to evaluate the deep learning model's ability to distinguish between violent and non-violent scenes. By training and testing the model with images from both categories, researchers can assess its accuracy and robustness in classifying physical violence in various contexts. Figure 1: Summary of the main components of the proposed active learning approach. The diversity of images available on Pexels allows researchers to select a representative sample of non-violent scenes that reflect the complexity of the real world. This ensures that the model learns to identify physical violence and differentiate it from everyday situations that could be misinterpreted as violent. 4. Smart-City CCTV Violence Detection (SCVD) dataset: The SCVD dataset offers a unique compilation of video footage captured through closed-circuit television (CCTV) systems deployed in urban settings [60]. This dataset accounts for variations in viewing angles, image quality, and recording contexts inherent to CCTV footage, which can significantly impact the effectiveness of violence detection algorithms. Notably, the SCVD dataset introduces a specific category dedicated to the identification of weapons, broadening the scope of violence detection to include any objects that could be employed harmfully. This expanded perspective enhances the classifier's ability to detect threats in urban environments and provides a different perspective on potential risks. Additionally, licensing the SCVD dataset under the \"CC BY-NC-SA 4.0\" license promotes collaboration and knowledge sharing within the research community while ensuring proper attribution to the original authors. The heterogeneity in the collection of images is intended to simulate the variability of situations that artificial intelligence systems could face in real-world applications, from security systems to developments in artificial intelligence aimed at understanding complex social contexts. It is important to note that all images included in the datasets used in this work are in the public domain.