Human Pose Estimation using Automated Multi-Camera Calibration

Abstract

3D human joint estimation is essential for enabling effective human-robot interaction in construction automation, facilitating precise monitoring of worker movements to enhance safety, ergonomics, and operational efficiency. Vision-based systems utilizing multi-camera setups offer diverse perspectives to address challenges such as occlusions, projection ambiguities, and sensor noise. However, these systems depend heavily on accurate camera calibration to align views and synchronize measurements. Traditional manual calibration methods are time-consuming, labor-intensive, and prone to human error, making them unsuitable for dynamic construction sites where frequent camera repositioning is required due to shifting conditions and tasks. This study proposes a novel framework that leverages external marker detection for real-time, automated camera calibration, eliminating the need for manual intervention. This approach significantly reduces setup time, minimizes errors, and ensures reliable performance in rapidly changing environments. Furthermore, the framework integrates an Extended Kalman Filter (EKF) to fuse 2D joint locations from multiple cameras, effectively handling sensor noise and the nonlinear nature of human motion. By combining marker-based calibration with EKF-based fusion, the proposed framework delivers a robust and automated solution for 3D human joint estimation, enhancing safety, efficiency, and adaptability in construction automation applications.