Fusion-Based Approach to Enhance Markerless Motion Capture Accuracy for On-Site Analysis

Markerless motion capture systems offer the advantage of non-intrusive and on-site motion analysis, but they often suffer from limited accuracy compared to marker-based systems. In this paper, we propose a novel fusion-based approach to enhance the accuracy of on-site motion analysis using OpenPose, a popular markerless motion tracking framework. The proposed method combines data obtained from OpenPose with a marker-based Regions of Interest (ROIs) detection method, aiming to improve the accuracy and reliability of markerless motion capture for on-site applications. Multiple cameras are utilized for 3D reconstruction to refine the joint positions initially provided by OpenPose. The obtained results exhibit a significant improvement in limb length and angle measurements, achieving root mean square errors (RMSE) of less than 32.7mm and 7.61 \(^\circ \) , respectively, after correction. These findings outperformed the accuracy achieved by OpenPose prior to employing our fusion-based approach.