## 2 Related Work  
In the context of smart cities, public safety represents a critical area of focus, prompting the academic community to develop various applications to address this challenge. One such initiative is Safest PATH [14], which aims to enhance pedestrian safety through the utilization of underutilized urban resources, including surveillance cameras and computer-equipped vehicles. Additional studies have concentrated on developing spatio-temporal prediction models based on time series analysis to identify high-risk areas and predict crime trends. These models, which have been evaluated using real data from cities such as New York and Chicago [15], have demonstrated performance improvements of up to 5% compared to similar approaches. For example, in [16], the Holt exponential smoothing method with monthly seasonality was employed and tested in Pittsburgh, Pennsylvania, showing that these models provide a more consistent level of accuracy compared to traditional policing practices. Another related solution is CriClust [17], a platform designed to identify patterns in crime series using a hybrid clustering model that incorporates geometric projection and a dual mural scheme. The results indicated that the model reliably identified inherent crime patterns within the dataset and scaled effectively with varying data volumes. There is a strong emphasis on developing solutions using deep learning techniques that incorporate both spatial and temporal dimensions. A notable example is the development of a weakly supervised learning model based on Multiple-Instance Learning (MIL), as proposed by [18]. In this approach, videos are labeled at the video level rather than on a segment-by-segment basis. Using a dataset comprising 1900 surveillance videos, this method demonstrated significant improvements in anomaly detection, achieving an Area Under the Curve (AUC) of 75.41%, outperforming conventional methods. These findings are of particular significance to the academic community, as they establish a solid foundation for addressing public safety concerns through the automated detection of anomalous events in real-world scenarios. Further research has investigated novel perspectives on the MIL approach utilizing graph convolutional neural networks (GCNs) [19], with some models demonstrating accuracies exceeding 80%. It has been suggested, however, that further optimization of classification methods could yield even better results. Additionally, a new model was developed to address the limitations associated with geometric transformations in CNNs by incorporating a deformable convolution module [20]. The Multiple-Instance Learning (MIL) approach has been extensively studied in recent years, with the RTFM (Robust Temporal Feature Magnitude Learning) method demonstrating considerable efficacy in addressing its inherent limitations. RTFM enhances traditional MIL methods by employing a temporal feature magnitude learning function, which improves the discrimination between normal and anomalous segments in weakly supervised video datasets. This method has been evaluated on benchmark datasets such as UCF-Crime, achieving an anomaly detection accuracy of up to 98.6%. Furthermore, RTFM's efficiency in processing training samples renders it suitable for real-time surveillance applications, providing significant performance improvements over conventional MIL approaches. Deep Autoencoders (AEs) are also widely utilized in anomaly detection, particularly due to their capacity to learn latent representations of normal data and identify deviations from expected behavior. Advanced approaches, including Dynamic Prototype Units (DPU) and meta-learning techniques [21], have substantially enhanced this process by enabling rapid adaptation to novel environments while avoiding overgeneralization. These methods have demonstrated effective performance, achieving accuracies exceeding 85% in anomaly detection, and have also exhibited rapid inference times, making them particularly suitable for real-time video surveillance systems.