## 1 Introduction
One prevalent approach involves employing these pre-trained models for spatial feature extraction, followed by a secondary network responsible for classification. Such methodologies have demonstrated precision percentages reaching up to 96% [16]. Another noteworthy implementation integrates ConvLSTM for temporal feature extraction and 3D ResNet50, 3D ResNet101, and 3D ResNet152 architectures for spatial feature extraction in conjunction with the UCF-Crime dataset, achieving a precision rate of 96% [17]. Recent advancements include a convolutional attention module (CBAM) to discern the dynamics among individuals requiring detection [18]. Other methods exploredfor violence detection involve feature extraction through optical flow, as demonstrated in the work presented in Ref. [19]. [PERSON] et al. [20] employ optical flow as input, preceding the utilization of a spatial-temporal codifier with DenseNet121 and a bidirectional layer of 2D convolutional LSTM (BiConvLSTM2D), resulting in an accuracy of 99%. Similarly, [PERSON] [21] experimented on four public violence detection datasets: Movies, Hockey, Crowd, and RWF-2000. The authors adopt a comparable approach, extracting two optical flows, concatenating them, and feeding them into the classifier, achieving precision rates of 0.86 and 1.0 for the Movies and RWF-2000 datasets, respectively. Furthermore, several studies have focused on addressing the violence detection challenge through a multi-class approach, utilizing the UCF-Crime dataset to more precisely distinguish between various types of violence [22]. Similarly, [PERSON] [23] examines four variants (Binary, UCF-Crime original dataset containing 14 classes, 13 abnormal events grouped as one anomalous class, 4 MajorCat, and NREF) of the same dataset (UCF-Crime). Likewise, [PERSON] [24] experimented with cartoon animated clips to classify violence into three classes: safe, fantasy violence, and sexual nudity. The utilization of data sourced from a variety of channels, including online social networks, public datasets, and video surveillance cameras (with or without sound), has emerged as a trend aimed at cultivating more robust models across diverse environments [2; 16; 25]. Nevertheless, as new videos in different scenarios are generated daily, relying solely on robust yet static models trained with diverse datasets still needs to be improved. The unprecedented volume of available data presents a significant opportunity for developing deep learning models; however, it also poses challenges such as the scalability of traditional models, usability, and adaptability [26]. Moreover, a critical challenge associated with this abundance of data is converting raw data into high-quality labeled data, which is essential for enhancing predictive models' accuracy. It is crucial to recognize that constructing a training set entails a potentially computationally intensive process in terms of both time and resources [27]. Active learning has emerged as a collaborative strategy that involves human participation alongside machine learning algorithms to iteratively construct predictive models and training sets. This iterative approach mitigates the costs of acquiring labeled data while concurrently enhancing the prediction accuracy of machine learning models [28]. Moreover, active learning facilitates the incorporation of new information into datasets utilized by automatic violence detection models. Active learning has been studied for several years; however, there have been few studies, although it is a very important topic in machine learning, especially now that many data exist but there are few high-quality datasets [29]. Today, it remains a focus of study in the academic research community (for example, see Refs. [30; 31; 32]) because of its utility in building robust machine learning models and obtaining high-quality labeled datasets.