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Content Provider | IET Digital Library |
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Author | Mehralian, Mohammad Amin Soryani, Mohsen |
Abstract | In real-world applications the perspective-n-point (PnP) problem should generally be applied to a sequence of images which a set of drift-prone features are tracked over time. In this study, the authors consider both the temporal dependency of camera poses and the uncertainty of features for the vision-only sequential camera pose estimation. Using the extended Kalman filter (EKF), a priori estimate of the camera pose is calculated from the camera motion model and then it is corrected by minimising the reprojection error of the reference points. Applying probabilistic approach also provides the covariance of the pose parameters which helps to measure the reliability of the estimated parameters. Experimental results, using both synthetic and real data, demonstrate that the proposed method improves the robustness of the camera pose estimation, in the presence of tracking error and feature matching outliers, compared to the state of the art. |
Starting Page | 3774 |
Ending Page | 3780 |
Page Count | 7 |
ISSN | 17519659 |
Volume Number | 14 |
e-ISSN | 17519667 |
Issue Number | Issue 15, Dec (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/14/15 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0606 |
Journal | IET Image Processing |
Publisher Date | 2020-12-04 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | A Priori Estimate Camera Camera Motion Model Camera Pose Computer Vision Computer Vision And Image Processing Technique Drift-prone Features Estimation Theory Extended Kalman Filter Feature Matching Outliers Image Matching Image Recognition Kalman Filter Nonlinear Filter Optical, Image And Video Signal Processing Perspective-n-point Problem Pose Estimation Real-world Application Temporal Dependency Vision-only Sequential Camera |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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