## 1 Introduction
Moreover, scaling these models to diverse urban environments with varying levels of complexity and infrastructure remains challenging. This study addresses some of these limitations by developing a novel crime detection dataset that encompasses a broader range of real-world scenarios, focusing specifically on crimes against individuals under different conditions, and by implementing an evaluation framework to validate model performance across varying environmental contexts. Researchers have proposed various architectures to process the vast amounts of information collected from cities [10]. These proposals benefit not only individual citizens but also the overall functioning and well-being of urban areas. Privacy remains a critical issue when handling citizens' personal data, and many urban surveillance systems are underutilized due to the overwhelming volume of information that cannot be effectively processed by human operators. There is substantial interest in leveraging this information to develop datasets, such as the Large-scale Anomaly Detection (LAD) dataset [11], to train machine learning models that assist in crime prevention. Video anomaly detection (VAD) aims to identify unusual patterns of behavior or appearance. This is an ongoing research area that contributes to public safety by exploiting the data collected by surveillance systems [12]. Anomalous events in videos are generally infrequent, which makes anomaly detection challenging due to the limited availability of labeled data. One approach to addressing this is Multiple-Instance Learning (MIL), which focuses on identifying events or labels for segments of a video treated as a set of instances or fragments. Deep learning models, particularly those involving 3D convolutional neural networks (CNNs), have been combined with MIL techniques, resulting in an 8% improvement compared to conventional models [13]. In this context, 3D CNNs extend the capabilities of traditional 2D CNNs--widely used in image processing--by operating on image sequences (videos) or volumetric data, incorporating both spatial and temporal dimensions. In the context of crime detection in videos, these networks facilitate the analysis of motion patterns, changes, and evolution over time, allowing for a more comprehensive understanding of dynamic scenes. This literature review has shown that current methods for crime detection, which often rely on machine learning and deep learning techniques, face significant challenges when applied in real-world environments. A major limitation is the quality of the datasets used for training these models. Existing datasets are frequently limited in both size and diversity, meaning that they do not adequately capture the wide range of scenarios and conditions encountered in real-world settings. For instance, variations in lighting, camera angles, occlusions, and diverse environmental contexts are often underrepresented, resulting in a lack of robustness in model performance. Furthermore, many datasets are curated under controlled conditions that do not reflect the complexities of urban environments, where crimes can occur under unpredictable circumstances. Consequently, these models struggle to generalize effectively, leading to decreased accuracy when applied beyond their training context. Addressing this gap requires the development of datasets that are more representative of real-world diversity, incorporating a broader range of crime-related scenarios to enhance model reliability and generalizability. To contribute to addressing the challenges of citizen security, this paper presents the development of a novel dataset specifically designed for detecting crimes against individuals in video footage. Unlike other, more general datasets, this dataset focuses exclusively on incidents such as robberies, assaults, and physical altercations. The primary objective of this study is to evaluate the effectiveness of the newly developed dataset through the application of existing deep learning models.