Among the vast range of tasks that modern advanced video surveillance systems face, the dominant position is occupied by the task of tracing various objects in the video stream, which is one of the fundamental problems in the field of video analytics. Numerous studies have shown that, despite the dynamism of processes in the field of information technology and the introduction of various tools and methods, the task of object maintenance still remains relevant and requires further improvement of previously developed algorithms in order to eliminate some inherent disadvantages of these algorithms, systematization of techniques and methods and the development of new systems and approaches. The presented article describes the process of step-by-step development of an algorithm for tracking human movements in a video stream based on the analysis of color groups. The key stages of this algorithm are: the selection of certain frames when dividing the video stream, the selection of the object under study, which is further subjected to a digital processing procedure, the basis of which is to obtain information about color groups, their average values and percentages of their occupancy relative to the object under study. This information is used for the procedure of searching, detecting and recognizing the selected object with an additional function of predicting the direction of movement on video frames, the result of which is the formation of the entire picture of the movement of the person under study. The materials presented in this paper may be of interest to specialists whose research focuses on issues related to the automated acquisition of certain data in the analysis of various images and videos.

Keywords: surveillance cameras, u2– net neural network, rembg library, pattern recognition, clothing recognition, delta E, tracing, direction prediction, object detection, tracking, mathematical statistics, predicted area, RGB pixels

Currently, tracing the movements of various objects (in particular a person) occupies a central place in video surveillance and video analytics systems. It is a system for tracking people's movements by localizing their positions on each frame within the entire video stream and is the basis of many intellectual computer vision systems. The purpose of this article is to develop a new algorithm for tracing human movements in a video stream with the possibility of selecting motion trajectories. The main stages of the algorithm include: dividing the video into frames with a difference of one second, selecting the person under study in the video stream, implementing a digital processing process based on recognizing the clothes of the person under study and obtaining its color histogram, predicting localization and recognizing the person under study on all subsequent frames of the video stream using the developed methods of forecasting the direction of movement of this object. The output data of the proposed algorithm is used in the procedure of forming and displaying a general picture of the movement of a particular person within the entire video stream. The information and materials contained in this article may be of interest to specialists and experts who, in their work, pay special attention to data processing when analyzing fragments of the video stream.

Keywords: surveillance cameras, u2– net neural network, rembg library, pattern recognition, clothing recognition, delta E, tracing, direction prediction, object detection, tracking, mathematical statistics, predicted area, RGB pixels

This article discusses the practical implementation of the self sovereign system based on the technology of a distributed decentralized data registry, also known as blockchain. An implementation of the system based on the Proof of Stake (PoS) consensus-building mechanism is presented, which provides a number of advantages over alternative implementations described in the literature. The results of measuring system performance in comparison with known implementations based on Proof of Work (PoW) are presented, confirming the high efficiency of the proposed solution.

Keywords: decentralized, user-centric, identity-based encryption, blockchain, self Sovereign identity system