3D Human Motion Capture Method Based on Computer Vision

Abstract

Introduction. The analysis of approaches to tracking the human body identified problems when capturing movements in a three-dimensional coordinate system. The prospects of motion capture systems based on computer vision are noted. In existing  studies  on  markerless  motion  capture  systems,  positioning  is  considered  only  in  two-dimensional  space. Therefore, the research objective is to increase the accuracy of determining the coordinates of the human body in three-dimensional  coordinates  through  developing  a  motion  capture  method  based  on  computer  vision  and  triangulation algorithms.Materials and Methods.  A  method  of  motion  capture  was  presented,  including  calibration  of  several  cameras  and formalization of procedures for detecting a person in a frame using a convolutional neural network. Based on the skeletal points obtained from the neural network, a three-dimensional reconstruction of the human body model was carried out using various triangulation algorithms.Results. Experimental studies have been carried out comparing four triangulation algorithms: direct linear transfer, linear least squares method, L2 triangulation, and polynomial methods. The optimal triangulation algorithm (polynomial) was determined, providing an error of no more than 2.5 pixels or 1.67 centimeters.Discussion and Conclusion. The shortcomings of existing motion capture systems were revealed. The proposed method was aimed at improving the accuracy of motion capture in three-dimensional coordinates using computer vision. The results obtained were integrated into the human body positioning software in three-dimensional coordinates for use in virtual simulators, motion capture systems and remote monitoring.