## 1 Introduction  
In recent years, the automatic detection of physical violence has been a challenge in human activity recognition because it involves a continuous analysis of human behavior [1]. Analysis at the frame level of a video has been widely studied for detecting physical violence. It is a process by which each individual frame of a video is analyzed and examined to identify signs of violent or aggressive behavior. This analysis involves looking for specific visual indicators, such as sudden movements, punching gestures, physical contact between people, or other patterns that may suggest a violent action. In performing this on a frame-by-frame basis, the system attempts to detect and categorize instances of violence as they occur over time, allowing for a detailed and granular understanding of the activity in the video without extracting temporal features [2]. Since the early 2000s, the analysis of video at frame level has demonstrated that it is possible to detect violence in images using only spatial feature extraction, using techniques such as LPB [3], HOG [4], FAST [5], SURF [6], SIFT [7], BRISK [8], and ORB [9], because of their ability to capture robust and distinctive spatial features that are invariant to common transformations such as scale, rotation, and illumination. These techniques identify key points in images that reflect crucial local details, such as edges, corners, and textures, essential for recognizing complex visual patterns associated with violence. The bag-of-words (BoW) technique, which works at the frame level of a video, has been used to compactly and robustly represent the extracted local features, allowing capture of the distribution of local visual patterns that characterize physical violence without the need for exact spatial arrangement. [PERSON] [10] used BoW to identify violence in a dataset containing 500 images with violent scenes and 1500 images without violence. When applying BoW to SIFT features, they achieved an accuracy of \(85.7\%\pm 1.4\%\). When using HOG features, the accuracy was \(84.3\%\pm 1.6\%\). In contrast, with LBP features, they achieved an accuracy of \(90.1\%\pm 1.5\%\), demonstrating that combining robust spatial features with effective BoW representation enables accurate detection of physical violence in images. However, despite the success achieved by analysis at the frame level of video, detecting physical violence in videos is still a significant challenge. Recently, it has been addressed using deep learning (DL) techniques, mainly artificial neural networks (ANNs). This approach aims to enhance the efficiency of classifying physical violence scenes extracted from various video sources, such as closed-circuit television systems (CCTV), smartphones, and other digital cameras used for surveillance tasks. Consequently, developing such methodologies has expanded the available tools, reducing the human effort required to identify physical violence situations accurately. Although the current literature and state-of-the-art research boast various works focused on achieving high accuracy rates in violence detection within videos, typically ranging between 90% and 99% (see Ref. [11]), it is worth noting that many of these models are tailored to specific datasets, which work only in those particular scenarios. Convolutional neural networks (CNNs) stand out as the predominant models employed for violence classification in videos at the frame level, with some of the most notable pre-trained networks being VGG16 [12], VGG19, DenseNet18, 26, 50, 101, 152, ResNet2 38 [13], ResNet18, 34, ResNet50 [14], EfficientNet-B7, InceptionB3, and MobileNetV2 [15]. Although those models were proposed some years ago, they are still the focus of research on image classification tasks, as discussed above. Strides in violence detection can be attributed to using pre-trained neural networks and the emergence of hybrid models.