## 6 Conclusions  
Contribution to the Development of Crime-Specific Datasets: This study contributes by creating a dataset focused on crimes against individuals, including robberies, assaults, and fights, which improves upon existing general-purpose datasets. The inclusion of temporal labeling and a range of scenarios provides a resource for training deep learning models aimed at real-time public safety applications. Innovation in the Use of 3D Neural Networks for Crime Video Analysis: The implemented model, based on 3D convolutional neural networks (3D CNNs), demonstrates accuracy in detecting criminal events (ranging from 94% to 95%) and shows the capability of this approach to capture the temporal and spatial dynamics of video sequences. This technique advances the use of deep learning models for surveillance and highlights its potential to enhance public security systems. Optimization of Processing and Scalability in Surveillance Systems: The experiments demonstrate that reducing video resolution and sample length does not significantly affect model accuracy, allowing for improved computational performance without compromising crime detection. This optimization supports the system's implementation in real-time surveillance environments and highlights its scalability for urban applications. Contribution to Public Safety through Real-Time Detection: The developed model, with a response time of 90 ms, proves suitable for the real-time detection of criminal activities, making it a viable tool for smart city implementation. This detection capability, combined with reduced false positives and negatives, offers a solution for crime prevention and resource optimization in public safety systems. Future WorkOne of the main areas of future work is the implementation of real-world tests to validate the effectiveness of the model under dynamic and complex conditions. While the results obtained in controlled laboratory environments are promising, it is necessary to evaluate the system's performance in urban surveillance scenarios, where conditions vary drastically in terms of lighting, movement of people and vehicles, and camera placement. It is recommended to carry out pilot deployments in collaboration with local authorities in urban areas with high crime incidence. These trials will allow for the analysis of the model's behavior with real-time data and in unforeseen situations, such as sudden changes in weather conditions or equipment failures. Through these tests, the system can be fine-tuned to minimize false positives and false negatives in real-world situations, optimizing its ability to detect crimes accurately and efficiently. Another important aspect is the integration of the system with existing surveillance infrastructure, such as public and private security cameras. It is essential to evaluate the model's interoperability with various platforms and devices to ensure its applicability and scalability in smart cities. These tests will also help define the technical requirements for large-scale implementation and will allow for performance adjustments based on available resources, such as cloud processing capacity or local servers. Finally, it is essential to conduct longitudinal studies to measure the system's impact on crime prevention and reduction in test areas. Analyzing metrics such as law enforcement response rates, the number of crimes detected, and the efficiency of police resource use will be crucial. These studies are key to determining the added value of the system in terms of public safety and justifying its large-scale adoption. Conceptualization, J.C.L.L., F.B.M. and L.A.F.B. ; methodology, J.C.L.L. ; software, J.C.L.L. ; validation, J.C.L.L., F.B.M. and L.A.F.B. ; formal analysis, J.C.L.L. ; investigation, J.C.L.L. ; resources, F.B.M. and L.A.F.B. ; data curation, J.C.L.L. ; writing--original draft preparation, F.B.M. and L.A.F.B. ; writing--review and editing, F.B.M. and L.A.F.B. ; visualization, J.C.L.L. ; supervision, F.B.M. and L.A.F.B. ; project administration, F.B.M.