An algorithm for tracking human movements in a video stream based on the color group matching method
Abstract
An algorithm for tracking human movements in a video stream based on the color group matching method
Incoming article date: 04.11.2023Among 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