## 2 Related Work
The UCFCrime dataset [18], developed by the University of Central Florida, is widely used for anomaly detection in surveillance videos. It consists of 1900 untrimmed videos with a total duration of 128 h, capturing real-world incidents involving 13 types of anomalies, such as abuse, arrests, accidents, vandalism, robberies, and explosions. This dataset is particularly valuable due to its variety and the realism of the anomalies captured, making it an essential resource for researching crime and anomaly detection in videos. Numerous studies have employed UCFCrime to develop anomaly detection techniques, and additional subsets like UCF-Crime2 Local [43] and HR-Crime [44] have been established. These subsets focus on human-related anomalies, excluding videos with technical issues (e.g., focus problems) or those concentrating on specific anomalies. XD-Violence [23] is an extensive dataset that integrates both visual and audio information for detecting violence in untrimmed videos. It comprises 217 h of footage across 4754 untrimmed videos, covering six categories of violence: abuse, car accidents, explosions, fights, riots, and shootings. The dataset was compiled from films and online videos sourced from YouTube. While the videos are labeled at the video level, frame-level annotations are available in the test set, enabling more precise temporal event detection. XD-Violence is regarded as a benchmark for violence detection, alongside other datasets such as LAD [11], RWF-2000 (Real-World Fighting) [45], and Real-Life Violence Situations (RLVS) [46]. LAD was designed with frame-level labels to facilitate the development of supervised learning methods. Additional datasets, such as Ubi Fights [47] and CCTV-Fights [48], focus on the detection of violence and abnormal events like fights and robberies. These datasets are essential for developing video surveillance technologies that can enhance public safety and assist with crime prevention. More comprehensive information on these datasets is provided in Table 1.  
The datasets discussed above encompass crimes against individuals, which represent a significant public safety concern in Colombia. These datasets cover a range of categories, including fights, vandalism, and robberies, as seen in UCFCrime, XD-Violence, LAD, and others, providing crucial labels necessary for the training process. The data contained in these datasets are instrumental for machine learning models to learn relevant patterns and make accurate predictions in real-time applications. However, many publicly available datasets lack representativeness with respect to the most prevalent types of crimes in Colombia, such as robberies, which remain one of the country's primary security issues. Additionally, some datasets suffer from class imbalance and lack diversity in the scenariosand contexts represented, complicating the ability of models to generalize effectively to real-world urban environments. Furthermore, certain datasets lack detailed and consistent temporal labeling, which is crucial for training models that aim to detect crimes accurately in video sequences. This paper introduces CrimeDetection, a novel dataset specifically designed to address crimes against individuals. Compiled from multiple sources and meticulously labeled, this dataset provides a foundational resource for training and evaluating machine learning models aimed at detecting criminal activities in public safety contexts. Furthermore, this study presents the implementation of a deep learning model to evaluate the efficacy of the newly developed dataset for crime detection purposes.