A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

Abstract

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.

1. Introduction

Colombia is a country with approximately 49 million inhabitants, 77% of which live in cities [1], and as in many Latin American countries, some Colombian cities suffer from insecurity. To face this situation and guarantee the country’s sovereignty, the Colombian government has public security forces formed by the National Army, the National Navy and the Air Force, which have the responsibility to secure the borders of the country as well as ensure its sovereignty. Additionally, the Colombian National Police has the responsibility of security in the cities and of fighting against crime.

To ensure citizen security, the Colombian National Police has a force of 180,000 police officers, deployed across the national territory and several technological tools, such as Command and Control Information Systems (C2IS) [2,3] that centralize all the strategic information in real time, improving situational awareness [2,3] for making strategic decisions [3,4], such as the location of police officers and mobility of motorized units.

The C2IS centralizes the information in a physical place called the Command and Control Citizen Security Center (in Spanish: Centro de Comando y Control de Seguridad Ciudadana), where under a strict command line, the information is received by the C2IS operators and transmitted to the commanders of the National Police to make the most relevant operative decisions in the shortest time possible (Figure 1).

The C2IS shows georeferenced information using a Geographic Information System (GIS) of several subsystems [5], such as crime cases reported by emergency calls, the position of the police officers in the streets and real-time video from the video surveillance system [6].

However, this technological system has a weakness in the Video Surveillance Subsystem because of the discrepancy between the number of security cameras in the Colombian cities and the system operators, which hinders the detection of criminal events. In other words, there are many more cameras than system operators can handle, meaning that the video information arrives at the Command and Control Citizen Security Center but it cannot be processed fast enough by the police commanders, and as such, they cannot take the necessary tactical decisions.

Bearing this in mind, this paper shows a Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to a Command and Control Citizen Security Center. This system uses a novel method for detecting criminal actions, which applies an object detector based on Faster Region-Based Convolutional Network (R-CNN) as a detector of criminal actions. This innovative application of Faster R-CNN as a criminal action detector was achieved by training and adjusting the system for criminal activities detection using data extracted from the Command and Control Center of the Colombian National Police.

This novel method automates the detection of criminal events captured by the video surveillance subsystem, generating alarms that will be analyzed by the C2IS operators, improving situational awareness of the police commanders present at the Command and Control Citizen Security Center.

2. Related Work in Crime Events Video Detection

In computer vision, there are many techniques and applications which could be relevant for the operators of the C2IS of the National Police, for instance, the detection of pedestrians, the detection of trajectories, background and shadow removing [7], and facial biometrics.

There are already several approaches to detect crimes and violence in video analysis, as shown by [8,9,10,11]. However, the Colombian National Police does not implement any method for the specific case of the detection of criminal events. The available solutions are not applicable because most of the cameras of the video surveillance system installed in Colombian cities are mobile (Pan–Tilt–Zoom Dome), which makes it difficult to use conventional video analysis techniques focused on human action recognition because most of these methods are based on trajectory [12,13,14,15] or movement analysis [16,17,18] and camera movements interfere with these kinds of studies.

Owing to this, we decided to explore innovative techniques independent of the abrupt movement of video cameras, which perform a frame-by-frame analysis without independence between video frames.

Bearing this in mind, we discarded all the techniques based on trajectory detection and used prediction filters or metadata included in the video files, focusing on techniques that could take advantage of hardware’s capabilities for parallel processing. As such, the criminal events detection system was developed using Deep Learning techniques.

Taking into account the technological developments of recent years, Deep Learning has become the most relevant technology for video analysis and has an advantage over the other technologies analyzed for this project: each video frame is analyzed and processed independently of all the others without temporary interdependence, which makes Deep Learning perfect for video analysis from mobile cameras such as those used in this project.

To choose the Deep Learning Models, we studied factors such as the processing time of each video frame, accuracy and model robustness. Therefore, several detection techniques were studied, such as R-CNN (Region-Based Convolutional Network) [19], YOLO (You Only Look Once) [20], Fast R-CNN (Fast Region-Based Convolutional Network) [21,22] and Faster R-CNN (Faster Region-Based Convolutional Network) [23,24] (

Table 1. Deep Learning object detection models relative comparison.

Object Detector Model	Average Accuracy	Average Processing Time	Model Deployment Level (Number of Works Related)
R-CNN	High	High	Medium
Fast R-CNN	High	Medium	Medium
Faster R-CNN	Very High	Very Low	Very High
SSD	Very High	Very Low	Very High
YOLO	Very High	Very Low	Very High

). After analyzing the advantages and disadvantages of each technique, Faster R-CNN was chosen to implement the system for criminal events detection in the system for the C2IS of the National Colombian Police due to the fact that it has an average timeout that was 250 times faster than R-CNN and 25 times faster than Fast R-CNN [22,25,26]. Furthermore, in recent work, models based on two stages like Faster R-CNN have had better accuracy and stability than models based on regression like YOLO [27,28] and SSD, which is of great importance because in this work, a novel application focused in action detection was given to an object detector model.

Analyzing real-time video frame-by-frame is a task with a very high computational cost. This is considerable taking into account the sheer amount of video cameras surveillance systems available in Colombian cities. Therefore, it is necessary that each video frame has a low computational cost and processing time to secure a future large-scale implementation.

With this in mind, several previous studies have been studied where real-time video is analyzed with security applications. Among these studies, one stands out [29], in which the authors performed video analysis from a video surveillance system using the Caffe Framework [30] and Nvidia cuDNN [31] without using a supercomputer. Another study that demonstrated the high performance of Faster R-CNN for video analysis in real time is [32], in which the video was processed at a rate of 110 frames per second. Another interesting study is [33], in which the authors made a system based on Faster R-CNN for the real-time detection of evidence in crime scenes. One last study to highlight is [34], in which the authors created an augmented reality based on Faster R-CNN implementation using a gaming laptop.

Other authors have carried out related relevant research, such as [35], in which fire smoke was detected from video sources; [36], which showed a fire detection system based on artificial intelligence; [37], which detected terrorist actions on videos; [38,39], that showed novel applications to object detection; [40,41], that showed an excellent tracking applications; [42] in which a Real-Time video analysis was made from several sources with interesting results in object tracking; [43] which proposed a secure framework for IoT Systems Using Probabilistic Image Encryption; [44] which showed an Edge-Computing Video Analytics system deployed in Liverpool, Australia; [45] where GPUs and Deep Learning were used for traffic prediction; [46] where a video monitor and a radiation detector in nuclear accidents were shown; [47] where an Efficient IoT-based Sensor Big Data system was detailed.

In addition to these, recently, interesting applications of Faster R-CNN have also been published, for example in [48], a novel application of visual questions answering by parameter prediction using Faster R-CNN was presented, [49] showed a modification of Faster R-CNN for vehicles detection which improves detection performance, in [50], a face detection application was presented in low light conditions using two-step Faster R-CNN processing, first detecting bodies and then detecting faces, [51] showed an application to detect illicit objects such as fire weapons and knives, analyzing terahertz imaging using Faster R-CNN as an object detector and [52] showed a Faster R-CNN application for the detection of insulators in high-power electrical transmission networks.

As shown previously, Deep Learning includes a variety of techniques in computer vision, which are suitable for the development of this work.

3. Novel Low Computational Cost Method for Criminal Activities Detection Using One-Frame Processing Object Detector

In many cases, the detection and recognition of human actions (like criminal actions) is done by analysis of movement [16,17,18,53,54] or trajectories [12,13,14,15], which implies the processing of several video frames. Nevertheless, when the video camera is mobile, it is very difficult to carry out the trajectory or movement analysis because camera movements may introduce noise to the trajectories or movements to be analyzed. In addition, in a Smart City application, the number of cameras could be hundreds or thousands, so motion or trajectories analysis involves processing several video frames for each detection, which would multiply the computational cost of a possible solution. It is necessary to analyze mobile cameras with the minimum computational cost possible because, in the Command and Control Citizen Security Center, thousands of cameras are pan–tilt–zoom domes and this makes it very difficult to perform a motion or trajectory analysis to detect criminal activities. On the other hand, since there are thousands of cameras, the computational cost becomes an extreme relevant factor.

For this reason, hours of video of criminal activities were studied and it was noted that all criminal activities have a characteristic gesture, such as threatening someone; therefore, we set out to analyze this characteristic gesture as an “object” so that it could be detected using techniques that are independent of camera movements and process only one video frame.

With this in mind, we propose a novel system called “Video Detection and Classification System (VD&CS)” in which Faster R-CNN is used in a hybrid way to detect objects used in criminal actions and criminal characteristic gestures treated as “objects”. Considering that criminal actions always have fixed gestures such as threatening the victim, it is possible to consider that this criminal action can be understood by the system as an “object”. This novel application has the potential to reduce the computational cost because only one video frame will be processed, compared to other action detection methods that must analyze several video frames [12,13,14,15,16,17,18,53,54]. With this novel method in mind, we proceeded with the system design and training.

3.1. Video Detection and Classification System (VD&CS)

The system proposed is based on a Faster Region-Based Convolutional Network (Faster R-CNN), involves two main parts: a region proposal network (RPN) and a Fast R-CNN [23] and it was developed using Matlab.

3.1.1. Region Proposal Network

The RPN is composed of a classifier and a regressor, and its aim is to predict whether, in a certain image region, a detectable object will exist or will be part of the background, as is shown in [23].

Regions of interest comprise short firearms, bladed weapons and street thefts, which are criminal actions but will be treated as objects in the training process.

In this case, the pre-trained CNN model AlexNet [55] was used as the core of the RPN. This CNN model is made up of Convolution layers, ReLU, Cross Channel Normalization layers, Max Pool layers, Fully Connected layers and Softmax layers, as shown in Figure 2.

Figure 3 shows AlexNet used as RPN core. It has less layers than models like VGG16 [56], VGG19 [56], GoogleNet [57] or ResNet [58]. Hence, AlexNet has a lower computational cost and requires less processing time per video frame [22] (further implementation details are provided in Section 5).

3.1.2. Fast Region-Based Convolutional Network

Fast R-CNN acts as a detector that uses the region proposals made by the RPN and also uses AlexNet (Figure 2) as the CNN of the core model to detect regions of interest for the system, which are short firearms, bladed weapons and street thefts (Figure 4).

3.1.3. Faster Region-Based Convolutional Network

Finally, RPN and Fast R-CNN are joined, forming a Faster R-CNN system (Figure 5) with the capacity of real-time video processing using AlexNet (Figure 2) as core.

3.2. VD&CS: Training Process

The system proposed based on Faster R-CNN, was trained using Matlab in a four-stage process, as outlined below.

3.2.1. Train RPN Initialized with AlexNet Using a New Dataset

At this stage, AlexNet, shown in Figure 2, is retrained inside the RPN, using transfer learning with a new dataset of 1124 images specially created to train the VD&CS (Figure 6). This dataset was created by manually analyzing several hours of video taken from the Command and Control Citizen Security Center and finding criminal actions to extract. The dataset has three classes of interest: short firearm, bladed weapons and street theft (action as object), and its bound boxes were manually marked for each image.

To improve the system performance, the training process data argumentation methods were used and as a result of the training procedure, in this stage, we obtained a feature map of the three classes mentioned above, from which the RPN is able to make proposals of possible regions of interest.

3.2.2. Train Fast R-CNN as a Detector Initialized with AlexNet Using the Region Proposal Extracted from the First Stage

In the second stage, a Fast R-CNN detector was trained using the initialized AlexNet as a starting point (Figure 7). The region proposals obtained by the RPN in the first stage were used as input to the Fast R-CNN to detect the three classes of interest.

3.2.3. RPN Fine Training Using Weights Obtained with Fast R-CNN Trained in the Second Stage

With the objective of increasing the RPN success rate, in the third stage, fine training of the RPN that was trained in the first stage is carried out (Figure 8). In this case, weights obtained from the training procedure of the Fast R-CNN during the second stage were used as initial values.

3.2.4. Fast R-CNN Fine Training Using Updated RPN

To improve the accuracy of the Fast R-CNN trained in the second stage, in this last stage, fine training was carried out using the results of the third stage, as shown in Figure 9.

Finally, Figure 10 shows a system capable at generating alarms detecting short weapons, blade weapons and street theft by analyzing just one video frame, which would reduce the computational cost compared to models based on analysis of movement or trajectories.

3.3. VD&CS: Testing

Once VD&CS was trained, its image processing time and accuracy were measured in order to evaluate its applicability to real scenarios of real-time video analysis. Two series of 500 images that were not used for training were used for testing using the same Hardware: MSI GT62VR-7RE with an Intel Core I7 7700HQ, 16 GB of DDR4 RAM, with a GPU NVIDIA GeForce GTX 1070 with 8 GB DDR5 VRAM).

Table 2. VD&CS 500 image tests.

Item Tested	Results Test 1	Results Test 2
Crime Event Detections	355	367
Failures	145	133
Undetected	87	80
False positive	58	53
Average processing time	0.03 s	0.03 s
FPS (Frames per second)	33 FPS	33 FPS
Undetected event rate	17.4%	16%
False positive rate	11.6%	10.6%
Accuracy	71%	73.4%

shows the obtained results. The used performance indicators were the average processing time per frame, accuracy, undetected event rate, false positive rate and frame rate per second (FPS).

According to previous results, the Confusion Matrix (

Table 3. VD&CS confusion matrix.

	Predictions
Observations	49.6% (True Positive)	11.6% (False Positive)
	17.4% (False Negative)	21.4% (True Negative)

) shows that VD&CS is useful for detecting criminal events in real-time video; its accuracy is within the parameters expected of a Faster R-CNN [23], taking into account that criminal actions were handled as objects within VD&CS, it confirms that VD&CS can be used for Criminal Activities Detection Applied to Command and Control Citizen Security Centers.

3.3.1. Real-Time Video Testing

Training and tests with real-time video were performed on a laptop MSI GT62VR-7RE with an Intel Core I7 7700HQ, 16 GB of DDR4 RAM, with a GPU NVIDIA GeForce GTX 1070 with 8 GB DDR5 VRAM.

Real-time video testing consisted of two main video sources; the first source contained pre-recorded videos obtained from the Colombian National Police video surveillance system and the second source was a set of videos captured in real time by a laptop camera.

In these two scenarios, excellent results were obtained with respect to the processing time of each image, ranging from 0.03 to 0.05 s. This allows real-time video processing at a rate of 20 to 33 FPS, which is adequate considering the video sources of the C2IS of Colombian National Police.

Regarding the system accuracy, we checked that it is free of overtraining as the tests done on the system were performed with images not used in the training process and their results were confirmed in the confusion matrix and the system accuracy it is within the range expected for a Faster R-CNN; however, the system is designed to be used in public safety applications, so it always requires human monitoring because the detections depend on the lighting conditions and the distance of the cameras to the object, in addition to the success rate of the Faster R-CNN; additionally, in previous studies [22], authors evaluated other CNN models of a greater depth by choosing AlexNet for its performance and simplicity.

However, it achieves excellent results in terms of triggering alarms when it detects criminal events, improving situational awareness in the Command and Control Citizen Security Center of Colombian National Police.

3.3.2. Computational Cost Comparation

As previously stated, several detection and recognition of human actions techniques consist of movement or trajectories analysis. These techniques must analyze several video frames to be able to recognize actions, for example, in [59,60,61], sets of six to eight images are analyzed to identify actions.

In order to have computational cost low enough to be deployed in thousands of cameras, VD&CS just processes one video frame to detect criminal actions, which achieves a low computational cost that could be deployed in embedded systems or in cloud architecture, reducing high deployment costs.

To analyze the computational cost, different CNN models in the VD&CS core were compared with another action detection technique proposed in [59]. The results are shown in

Table 4. VD&CS computational cost comparation.

Model	Average Processing Time	GPU	GPU Performance (Float 32)	Resolution (Pixels)
VD&CS (AlexNET)	0.03 s	Nvidia GTX 1070 MXM	6.738 TFLOPS	704 × 544
VD&CS (VGG-16)	0.23 s	Nvidia GTX 1070 MXM	6.738 TFLOPS	704 × 544
VD&CS (VGG-19)	0.28 s	Nvidia GTX 1070 MXM	6.738 TFLOPS	704 × 544
T-CNN	0.9 s	Nvidia GTX Titan X	6.691 TFLOPS	300 × 400

.

Therefore, assuming that GPUs have an equivalent performance and scaling the resolution of the video frames used in the tests, we consider deployments in cities like Bogotá where there are about 2880 Pan–Tilt–Zoom cameras (as of June 2019).

First, we analyzed computational cost measured TeraFlops and depict the variation of computational costs for processing of 2880 cameras (Figure 11).

We also analyzed the Hardware cost and power consumption, assuming a deployment using Nvidia embedded systems [62] (Figure 12 and Figure 13).

As Figure 11, Figure 12 and Figure 13 show, having thousands of video sources in a Low Processing Time System, the computational cost is a factor of extreme relevance, since the economic and energy costs could make the implementation not feasible, and for this reason VD&CS proves be appropriate in a Low Cost System.

3.4. VD&CS: Final System

Once the process of training and testing are completed, we propose the system shown in Figure 14 to be applied in a larger city architecture.

In this approach, the VD&CS runs in an environment independent of the operating system because it can be implemented using any framework or library that supports Faster R-CNN, such as Caffe [30], cuDNN [31], TensorFlow [63], TensorRT [64], Nvidia DeepStream SDK [65], which uses real-time video coming from the security cameras and uses GPU computational power to run. Finally, the VD&CS uses network interfaces to send the generated alarms to the Command and Control Citizen Security Center.

This system is expected to be applied in different scenarios based on cloud architectures or embedded systems compatible with IoT (Internet of Things) solutions [66,67].

4. Low Processing Time System Applied to Colombian National Police Command and Control Citizen Security Center

To propose a Low Processing Time System to detect criminal activities based on a real-time video analysis applied to National Police of Command and Control Citizen Security Center, we must consider the Colombian Police Command and Control objectives, as detailed below:

Situational awareness: Police commanders must know in detail and real-time the situation of citizen security in the field, supported by technological tools to make the best tactical decisions and guarantee the success of police operations that ensure citizen security.

Situation understanding: Improving situational awareness by improving crime detection, allows police commanders to gain a better understanding of the situation, helping to detect more complex behaviors of criminal gangs.

Decision making-improvement: Decisions made in the Command and Control Citizen Security Center can be life or death because many criminal acts involve firearms and violent acts; therefore, the proposed system will improve decision making because it will provide real-time information to commanders, improving the effectiveness of police operations.

Agility and efficiency improvement: As mentioned above, decisions made by the police can mean life or death. Therefore, the improvement offered by the proposed prototype to the agility and efficiency of police operations relies on information that is unknown by commanders, impeding the deployment of police officers in critical situations.

4.1. Decentralized Low Processing Time System for Criminal Activities Detection based on Real-time Video Analysis Applied to the Colombian National Police Command and Control Citizen Security Center

The Command and Control Citizen Security Center is formed of subsystems such as the emergency call attention system (123), Police Cases Monitoring and Control Information System (SECAD), Video Surveillance Subsystem and the crisis and command room. Command and Control Citizen Security is supported by telecom networks that can be owned by the National Police or belong to the local ISP (Internet Service Provider).

These subsystems have different types of operators which are in charge of specific tasks such as monitoring the citizen security video (Operators Video Surveillance system), answering emergency calls (123 Operators) and assigning and monitoring field cop to police cases (Dispatchers).

Another important part of the Command and Control Citizen Security Center is the crisis and command room, in which the police commanders make strategic decisions according to their situational awareness and situation understanding [2].

In this decentralized system, the VD&CS will be implemented in embedded systems with GPU capability such as Nvidia Jetson [62] or AMD Embedded Radeon™ [68]. Then, it will be installed in each citizen video surveillance camera, detecting criminal activities locally (Figure 15).

After each detection, alarms will be generated and will be sent by a network to the Video Surveillance Subsystem where operators can take actions to prevent and respond to criminal actions.

4.2. Centralized Low Processing Time System to Criminal Activities Detection Based on Real-Time Video Analysis Applied to Colombian National Police Command and Control Citizen Security Center

In contrast to the previously decentralized system shown before, in this case, the video will be processed in a centralized infrastructure with high computational power and GPU capabilities (Figure 16).

The datacenter runs the VD&CS individually for each video signal coming from each of the city video surveillance cameras, generating alarms when criminal activities are detected, and sends it back to the Video Surveillance Subsystem through the network, where operators can take actions to prevent and respond to criminal actions.

5. Possible Implementation and Limitations

Considering that the development of VD&CS was performed on a laptop using Matlab and Windows 10 and that an image processing rate of 20 to 30 frames per second was obtained, it is feasible to migrate the VD&CS to an environment with greater efficiency using the libraries optimized for Deep Learning, such as cuDNN [31], TensorFlow [63], TensorRT [64], Nvidia DeepStream SDK [65], further reducing the computational cost.

With this reduction in the computational cost, it would be possible to implement VD&CS in embedded systems, such as the Nvidia Jetson [62] and optimize the implementation using Nvidia DeepStream [65], to be installed directly in citizen security cameras and subsequently generate alerts upon the occurrence of criminal events which would be reported to the Command and Control Citizen Security Center of the Colombian National Police, like in the decentralized Low Processing Time System.

Currently, in June 2019 in Bogotá D.C., there are about 2880 Pan–Tilt–Zoom cameras that are monitored in the Citizen Security Control Center, and these domes generate around 22.4 Gbps of real-time video traffic. Given that currently, in Colombia, there is no cloud provider that has datacenters in the country, it would not be applicable to use cloud solutions with datacenters in the United States or Brazil because the international channel cost would be very high; therefore, in June 2019, the best solution is to use embedded systems, at least until a cloud provider provides a datacenter with GPU capability in Colombia.

The VD&CS limitations must be considered in future implementations because, like all systems based on Deep Learning, it is not 100% reliable and its precision is linked to critical factors such as lighting and partial obstructions, meaning that human supervision is necessary.

However, the implementation of this Low Processing Time System in a large-scale environment depends on the budget availability of the Government of Colombia.

6. Discussion and Future Application

VD&CS have proven to be effective in a hybrid operation as an object detector and the treatment of criminal actions as objects. If the characteristic gestures are identified in certain actions, it should be possible to use object detectors based on Deep Learning in various applications such as the detection of suspicious activities, fights, riots and more.

As shown above, several recent applications of Faster R-CNN have shown great performance as object detector [48,49,50,51,52], however, this work demonstrated that applying object detection techniques based on Deep Learning like Faster R-CNN in actions detection could be an alternative to action recognition based on analysis of trajectories or movements and could be applied more easily in highly mobile video environments, such as military operations, transportation, citizen security, and national security to name only a few, nevertheless, human supervision is always required, because after a while, the quantity of False Negatives and False Positive could drastically reduce the system effectiveness, which is very serious in safety applications.

In future research, we could identify human actions that could be recognized using object detectors based on Deep Learning.

These actions should have characteristic gestures like in the case of criminal activities, which always have recognizable gestures such as threatening the victim.

Although the system’s accuracy is around 70%, this percentage can be considered acceptable because the system is tolerant to the sudden movements of the Pan–Tilt–Zoom cameras of the Colombian National Police. It also shows that it is possible to use an object detector to detect criminal actions and in future applications, the system’s accuracy could be improved.

Further future research work consists of maximizing the recognition of human actions using an objects classifier, minimizing system failures. This can be achieved by building more complete datasets and experimenting with diverse Deep Learning techniques such as YOLO, and several CNN models such as ResNet, GoogleNet.

7. Conclusions

By applying the secure city architectures in command and control systems, situational awareness and situation understanding of police commanders will improve, as well as their agility and efficiency in decision making, thus improving the effectiveness of police operations and directly increasing citizen security.

During the development of the VD&CS, it has been proven that it is possible to improve situational awareness in the Command and Control Citizen Security Center of the Colombian National Police, triggering alarms of criminal events captured by the video surveillance system.

Reducing the computational cost for using Deep Learning or any other technique in citizen security applications is fundamental for achieving real-time performance and feasible implementation costs, especially given the amount of information generated by surveillance systems. The processing time is vital to achieve a real improvement of situational awareness.

The Low Processing Time System to Criminal Activities Detection Applied to a Command and Control Citizen Security Center could be deployed in Colombia because the VD&CS showed that it is possible to detect criminal actions using a Deep Learning Object Detector as long as the system is trained to detect actions (these actions must have characteristic gestures such as threatening the victim). Deep Learning can be a powerful tool in citizen security systems because it can automate the detection of situations of interest which can escape from system operator view in Command and Control Information System of a security agency such as the Colombian National Police.