MirrorCalib: Utilizing Human Pose Information for Mirror-based Virtual Camera Calibration
This addresses a specific calibration challenge in computer vision for exercise videos with mirrors, but it is incremental as it builds on existing methods like the eight-point algorithm and RANSAC.
The paper tackles the problem of estimating extrinsic parameters of a virtual camera relative to a real camera in exercise videos with a mirror, achieving a rotation error of 1.82° and a translation error of 69.51 mm, outperforming the state-of-the-art method.
In this paper, we present the novel task of estimating the extrinsic parameters of a virtual camera relative to a real camera in exercise videos with a mirror. This task poses a significant challenge in scenarios where the views from the real and mirrored cameras have no overlap or share salient features. To address this issue, prior knowledge of a human body and 2D joint locations are utilized to estimate the camera extrinsic parameters when a person is in front of a mirror. We devise a modified eight-point algorithm to obtain an initial estimation from 2D joint locations. The 2D joint locations are then refined subject to human body constraints. Finally, a RANSAC algorithm is employed to remove outliers by comparing their epipolar distances to a predetermined threshold. MirrorCalib achieves a rotation error of 1.82° and a translation error of 69.51 mm on a collected real-world dataset, which outperforms the state-of-art method.