Development of video control system training stand

Martynov Artur Mikhailovich Student of the Information Security Systems Department of Kazan National Research Technical University named after A.N. Tupolev-KAI 420015, Russia, Republic of Tatarstan, Kazan, Bolshaya Krasnaya str., 55 martynovartur022@gmail.com

Introduction

A modern higher education institution should prepare students for a successful career in a rapidly changing labor market. This requires not only the formation of professional skills, but also the development of important personal qualities among graduates. An important component of learning is the practical experience gained by students through working with real laboratory complexes. This allows students not only to acquire theoretical knowledge, but also to acquire practical skills in working with modern technical systems.

As an example, in the article ^[1], the authors presented the development of a security and fire system stand for conducting laboratory work by students. Students at this stand can physically connect the cables of five types of sensors, configure them using a receiving and control device (Astra-812 PRO), and test for operability ^[2]. This approach not only improves students' understanding of the technical foundations of security, but also provides them with the opportunity to simulate various types of systems used in buildings and infrastructure, which is a valuable experience for their future professional activities.

The development of a laboratory stand for video surveillance systems deserves special attention as part of the students' training. As part of the academic discipline "Technical means of protection", students have the opportunity to study the principles of operation and features of the installation of video monitoring systems. The laboratory stand should include a set of video cameras, a video processing server, as well as software for configuring and analyzing video data. This allows students to become familiar with the installation, configuration, troubleshooting processes, as well as video analysis methods in the context of security systems.

Thus, the use of laboratory stands in the educational process contributes to a deep understanding by students of the real conditions of working with video monitoring systems and other technical means of protection. This not only enhances their competencies, but also prepares them for a successful career in the field of security.

Description of the developed laboratory complex

The laboratory complex, designed to train students in the field of video surveillance systems, is an integrated system that includes several key components. The basis of the complex are Grundig surveillance cameras, which provide image capture. The data from the cameras is transmitted to the switchboard and then to the DVR, which is responsible for saving video footage. The switch plays an important role in processing and transferring data to the student's personal computer for video analysis and processing (Fig. 1).

Components of the laboratory complex:

1. CCTV cameras (1,2): the main devices for capturing images.

2. Switching panel (3): the place where the digital video signal is generated, which is then transmitted to the network switch.

3. Switching cabinet (4): contains the necessary equipment for connecting and processing video signals.

4. DVR (5): The device connected to the switch is responsible for recording and storing video streams.

5. Network switch (6): the central node of the system that provides data transfer between system components.

6. Student's computer (7): used for analyzing and processing video data received from cameras.

The video cameras (1,2) capture the image, after which the digital video signal is transmitted through the switching panel (3) to the network switch (6) located in the switching cabinet (4). The cameras are connected to the switch using a twisted pair of the fifth category. The video cameras are also connected to a 12 volt power supply via an adapter built into the laboratory's general power supply system.

The video camera contact switches are located on the switching panel (3), which allow you to control the connection of cameras. The DVR (5) installed in the switching cabinet (4) connects to the network switch (6) and records video streams coming from the cameras. The student's computer (7) connected to the switch allows students to analyze and process video data.

Figure (2) also shows the viewing areas of cameras 1 and 2 and the directions of movement of objects in the controlled area. This gives students an idea of how cameras work in real-world conditions and how to analyze data obtained from a video surveillance system.

Figure 1. Diagram of the laboratory complex

Figure 2. Layout of the components of the laboratory complex

The methodology of laboratory work on the topic "Introductory part. Description. Theory"

Figure 3. The methodology of the laboratory lesson "Introductory part. Description. Theory"

The first stage of the laboratory work is aimed at studying the theoretical aspects of video surveillance systems. Students will have to familiarize themselves with basic concepts, including the definition of video surveillance facilities, the features of video cameras, as well as the functions and tasks of switches and recorders. This knowledge forms the foundation for understanding the structure and principles of video surveillance systems ^[4].

At the practical stage of the laboratory work, students apply their theoretical knowledge by connecting the camera cables to the switchboard. This process includes preparing the equipment for operation, properly connecting the cables and then starting the system. It is important to note that safety when working with electronic devices is a priority, therefore, disconnecting the power supply of the switch before connecting the cables is mandatory ^[5].

Further, the training course includes the study of video surveillance system software. Students learn how to install and run programs, as well as master navigation through the software interface to view available cameras and search for a camera by IP address. These skills are critical for the management and monitoring of video surveillance systems.

After mastering the software, the stage of setting the parameters of the video camera follows. This process includes entering the camera settings menu and adjusting the parameters according to the requirements of the laboratory task. Setting up a camera allows students to understand how different parameters affect the quality and effectiveness of video surveillance.

At the end of the laboratory work, students analyze and summarize their activities, drawing conclusions about the acquired knowledge and practical skills. The basic concepts, connection procedures, principles of software operation and camera settings, studied and applied in the course of work, give students valuable experience that will contribute to their future careers in the field of technical security systems.

The methodology of laboratory work on the topic "Setting up. Practical connection of cameras"

Figure 4. The methodology of the laboratory lesson "Setup. Practical connection of cameras"

The first step in the laboratory work is to master the software used to control the video surveillance system. Students are offered a sequence of actions, starting with installing software on a computer and ending with searching and viewing available cameras in the system. The main focus is on understanding the process of the program and its interface, which is a key skill for further work with the system ^[6].

Actions for learning software:

- Software installation: Follow the instructions of the developer to install the software.

- Software startup: after installation, you need to run the program and familiarize yourself with its interface.

- Viewing the list of cameras: an important step is to familiarize yourself with the list of available cameras, which allows you to understand the structure of the video surveillance network.

- Camera search by IP address: Learn how to identify and locate cameras by IP addresses, which is necessary for network diagnostics and troubleshooting.

2. The principle of camera setup

The next stage of the laboratory work includes the practical adjustment of the parameters of the video camera. This process involves understanding the functions of the camera setup menu and the ability to adjust these settings according to the requirements of the task.

Actions to set up the camera:

- Access to the settings menu: log in to the camera settings menu via the software interface.

- Configuration of parameters: Select and adjust camera settings such as resolution, sensitivity, motion detection areas and other important functions.

- Save settings: After setting the necessary parameters, save the settings to ensure their continuous operation.

Mastering the techniques of connecting and configuring cameras allows students to acquire the practical skills necessary to work with video surveillance systems. In the course of laboratory work, they learn to analyze and solve problems related to the management and maintenance of video surveillance, which is an important part of their professional education.

The methodology of laboratory work on the topic "Software configuration. User recognition by face shape and size"

Figure 5. The methodology of the laboratory lesson "Software setup. User recognition by face shape and size"

1. Connection to video surveillance cameras

The first step is to install and prepare the software for controlling the video surveillance system. Students should learn how to connect to video cameras using IP addresses, which is the basis for subsequent configuration of facial recognition functions.

Connection procedure:

- Software installation: Follow the instructions to install the software.

- Software launch: open the video surveillance management program.

- Enter the IP address: Enter the IP address of the camera in the appropriate field of the program.

- Connection initiation: Press the "Connect" button to establish a connection with the camera.

2. Setting up face recognition

Next, the students proceed to setting up the face recognition parameters. This stage requires an understanding of the operation of various settings, such as image quality and recognition algorithms, which affect the efficiency and accuracy of the system ^[7].

Steps to set up face recognition:

- Selection of the recognition function: find and enable the face recognition function in the software settings.

- Parameter configuration: set the image quality and the size of the search area to optimize the recognition process.

- Algorithm selection: determine the most appropriate facial recognition algorithm available in the software.

3. Face recognition testing

After setting up the parameters, the system is tested to verify its ability to correctly recognize faces. This allows you to evaluate the correctness of the settings and the effectiveness of the recognition algorithm.

Testing procedure:

- Camera positioning: Point the camera at a person for recognition.

- Waiting for recognition: Wait for the automatic face recognition software.

- Checking the result: confirm the accuracy of the facial recognition program.

At the end of the laboratory work, students analyze the experience gained and draw conclusions about the learned skills. An important result is the acquisition of practical skills in the field of configuring and testing facial recognition functions in video surveillance systems, which is a significant skill in the modern context of security and video monitoring.

Program code for face recognition and search in the database

Facial recognition is one of the most promising areas in the field of computer vision and artificial intelligence. Facial recognition software allows you to identify or verify a person in a photo or video. In this article, let's look at an example of a program code that demonstrates the process of recognizing and matching a person's face with data in a database.

Libraries and tools used:

- cv2: one of the main Python libraries for working with images and videos.

- DLIB: A powerful machine learning library that includes pre-trained models for facial recognition.

- numpy: a library for efficient work with arrays of data.

The face recognition process:

1. Initialization of the face detector: dlib is used.get_frontal_face_detector() to load a pre-trained face detector.

2. Loading the database: The database stores encoded vectors of faces that can be mapped to the face in the image.

3. Image processing: The image is loaded and converted to grayscale to improve processing.

4. Face detection: The detector finds the faces in the image and returns their coordinates.

5. Face encoding: Each detected face is encoded into a vector that can be used for comparison with vectors in the database.

6. Comparison with the database: the program compares the encoded vectors of faces with the vectors in the database and finds the most similar vector.

7. Results: if there is a match, the program outputs the name of the corresponding person. Otherwise, it informs that the person has not been found.

Algorithmic features:

- Grayscale conversion: Reduces the complexity of the image, simplifying the task for the recognition algorithm.

- Face encoding: Converting faces into numeric vectors allows you to perform mathematical comparisons between different faces.

- Distance minimization: Using Euclidean distance to determine the greatest similarity between the encoded face and the faces in the database.

The sample code demonstrates the capabilities of modern machine learning and computer vision libraries for creating facial recognition systems.

import cv2

import numpy as np

# Downloading the Dlib facial recognition library

face_detector = dlib.get_frontal_face_detector()

# Loading the database of persons

database = {}

with open('database.csv', 'r') as f:

for line in f.readlines():

data = line.split(',')

database[data[0]] = np.array(data[1:])

# Uploading an image

img = cv2.imread('image.jpg')

# Convert the image to grayscale

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Finding the faces in the image

faces = face_detector(gray)

# We go through all the found faces

for face in faces:

# Getting the coordinates of the face

(x, y, w, h) = face.left(), face.top(), face.right(), face.bottom()

# Cutting out the face from the image

face_image = img[y:y + h, x:x + w]

# Encoding the face into a vector

face_encoded = face_recognition.face_encodings(face_image)^[0]

# Finding the most similar face in the database

best_match = None

min_distance = np.inf

for name, face_encoded in database.items():

distance = np.linalg.norm(face_encoded - face_encoded)

if distance < min_distance:

min_distance = distance

best_match = name

# Output the results

if best_match is not None:

print('Face found:', best_match)

else:

print('Face not found')

Developers can use this code as the basis for creating their own facial recognition applications, as well as for training and research projects in the field of artificial intelligence. The presented code can be developed using the principles of parallel programming, which are described in ^[8,9,10], this is the subject of future research.

Conclusion

As part of the training in the discipline "Technical means of protection", laboratory classes have taken a central place in the formation of professional competencies of students. They went through a comprehensive learning process, ranging from connecting CCTV cameras to complex face recognition software setup procedures.

The connection and configuration of video surveillance equipment allowed students to immerse themselves in the technical aspects of security, gaining knowledge about the types of cables and the correct connection of cameras. Then they moved on to mastering the software, learned how to control the video surveillance system, add and configure cameras, as well as work with the parameters of recording and displaying video information.

Face recognition setup has become one of the key learning points, where students have learned how to select and configure face recognition algorithms and create databases for face identification. This knowledge and skills open the doors for students to the world of modern security technologies, where the ability to work with software and databases is an important asset.

Summing up, we can say that the laboratory work carried out not only contributed to the deepening of theoretical knowledge, but also provided students with valuable practical skills. The ability to connect and configure video surveillance systems, manage software and perform face recognition – all this became the basis for their future careers in the field of security equipment and increased their readiness to apply these skills in everyday life.

Thus, teaching students how to work with video surveillance systems not only strengthens their technical literacy, but also prepares them to effectively solve problems in a rapidly changing technological world, emphasizing the importance of security and privacy in modern society.