## 2 Related Work
Anomaly detection using weak labels has been extensively studied in the context of crime. This technique can also be applied to other domains, such as detecting traffic-related issues, accidents, and other factors affecting public safety. A notable implementation is presented in [22], which proposes the Weakly Supervised Anomaly Localization (WSAL) model for detecting anomalies in videos using video-level labels. This approach focuses on the temporal localization of anomalous segments within video sequences, utilizing a Hierarchical Context Encoder (HCE) model that evaluates both dynamic variations and immediate segment semantics. The results obtained on the UCF-Crime dataset, as well as the novel Traffic Anomaly Detection (TAD) dataset, demonstrate substantial improvements over existing methods, achieving an AUC of 85.38% on UCF-Crime and 89.64% on TAD, thereby surpassing the state of the art in both cases. Similarly, [23] presents a multimodal approach to violence detection in videos, integrating both visual and auditory signals. This study introduces the \"XD-Violence\" dataset, which comprises 4754 untrimmed videos with weak labels alongside a neural network that fuses visual and auditory features. The results show an improvement in average precision (AP) of up to 3.5% compared to unimodal approaches. Additionally, the proposed architecture supports online detection with a minor reduction in performance, highlighting its potential applicability in real-time scenarios. Despite advancements in deep learning models, online crime detection continues to present significant challenges. One primary issue is the rarity and unpredictability of criminal incidents and anomalies, resulting in a scarcity of labeled data. This limitation constrains the models' ability to generalize effectively to real-world scenarios. Furthermore, online detection necessitates rapid processing times to ensure timely analysis and decision-making capabilities. The variability in crime scenes and environmental conditions, including factors such as lighting and camera angles, further complicates the development of robust solutions. Moreover, these systems must achieve high accuracy while minimizing false positives--an objective that becomes particularly challenging in situations involving unreliable or insufficiently labeled data. In [24], a comprehensive review of violence detection techniques is presented, aiming to elucidate their significance for public safety and crime prevention. The study provides an extensive overview of the most advanced methods for detecting violence in video content,categorizing them into three primary groups: traditional machine learning techniques, support vector machines (SVM), and deep learning. Furthermore, the review examines feature extraction techniques and object detection methods associated with each category, as well as the datasets and video attributes that play a critical role in the violence recognition process. Key challenges in automatic violence detection include the substantial variability in violent behaviors and contexts, which complicates the task of identifying consistent patterns, along with the need for improvements in accuracy, speed, and reliability to support real-time video surveillance systems. In [25], a detailed compilation of datasets, metrics, and techniques for action detection in unedited videos is presented, which is highly applicable to crime detection tasks. Action detection in these videos poses considerable challenges due to the rapid occurrence of events and the difficulty in acquiring sufficient training examples. Several datasets have been developed to facilitate the training and testing of action detection algorithms. Additionally, this work analyzes different methodologies from various perspectives, organizing them into distinct categories to enhance their comprehension and applicability in this domain.