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.