This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as “objects” to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.

This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities.

This article shows a novel geo-visualization method of dynamic spatiotemporal data that allows mobility and concentration of criminal activity to be study. The method was developed using, only and significantly, real data of Santiago de Cali (Colombia), collected by the Colombian National Police (PONAL). This method constitutes a tool that allows criminal influx to be analyzed by concentration, zone, time slot and date. In addition to the field experience of police commanders, it allows patterns of criminal activity to be detected, thereby enabling a better distribution and management of police resources allocated to crime deterrence, prevention and control. Additionally, it may be applied to the concepts of safe city and smart city of the PONAL within the architecture of Command and Control System (C2S) of Command and Control Centers for Public Safety. Furthermore, it contributes to a better situational awareness and improves the future projection, agility, efficiency and decision-making processes of police officers, which are all essential for fulfillment of police missions against crime. Finally, this was developed using an open source software, it can be adapted to any other city, be used with real-time data and be implemented, if necessary, with the geographic software of any other C2S.