MirrorCalib: Utilizing Human Pose Information for Mirror-based Virtual Camera Calibration
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911880230993920 |
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| author | Liao, Longyun Zheng, Rong Mitchell, Andrew |
| author_facet | Liao, Longyun Zheng, Rong Mitchell, Andrew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_02791 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MirrorCalib: Utilizing Human Pose Information for Mirror-based Virtual Camera Calibration Liao, Longyun Zheng, Rong Mitchell, Andrew Computer Vision and Pattern Recognition 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. |
| title | MirrorCalib: Utilizing Human Pose Information for Mirror-based Virtual Camera Calibration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.02791 |