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A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition
The camera is the main sensor of vison-based human activity recognition, and its high-precision calibration of distortion is an important prerequisite of the task. Current studies have shown that multi-parameter model methods achieve higher accuracy than traditional methods in the process of camera...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105339/ https://www.ncbi.nlm.nih.gov/pubmed/35591215 http://dx.doi.org/10.3390/s22093524 |
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author | Jin, Ziyi Li, Zhixue Gan, Tianyuan Fu, Zuoming Zhang, Chongan He, Zhongyu Zhang, Hong Wang, Peng Liu, Jiquan Ye, Xuesong |
author_facet | Jin, Ziyi Li, Zhixue Gan, Tianyuan Fu, Zuoming Zhang, Chongan He, Zhongyu Zhang, Hong Wang, Peng Liu, Jiquan Ye, Xuesong |
author_sort | Jin, Ziyi |
collection | PubMed |
description | The camera is the main sensor of vison-based human activity recognition, and its high-precision calibration of distortion is an important prerequisite of the task. Current studies have shown that multi-parameter model methods achieve higher accuracy than traditional methods in the process of camera calibration. However, these methods need hundreds or even thousands of images to optimize the camera model, which limits their practical use. Here, we propose a novel point-to-point camera distortion calibration method that requires only dozens of images to get a dense distortion rectification map. We have designed an objective function based on deformation between the original images and the projection of reference images, which can eliminate the effect of distortion when optimizing camera parameters. Dense features between the original images and the projection of the reference images are calculated by digital image correlation (DIC). Experiments indicate that our method obtains a comparable result with the multi-parameter model method using a large number of pictures, and contributes a 28.5% improvement to the reprojection error over the polynomial distortion model. |
format | Online Article Text |
id | pubmed-9105339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91053392022-05-14 A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition Jin, Ziyi Li, Zhixue Gan, Tianyuan Fu, Zuoming Zhang, Chongan He, Zhongyu Zhang, Hong Wang, Peng Liu, Jiquan Ye, Xuesong Sensors (Basel) Article The camera is the main sensor of vison-based human activity recognition, and its high-precision calibration of distortion is an important prerequisite of the task. Current studies have shown that multi-parameter model methods achieve higher accuracy than traditional methods in the process of camera calibration. However, these methods need hundreds or even thousands of images to optimize the camera model, which limits their practical use. Here, we propose a novel point-to-point camera distortion calibration method that requires only dozens of images to get a dense distortion rectification map. We have designed an objective function based on deformation between the original images and the projection of reference images, which can eliminate the effect of distortion when optimizing camera parameters. Dense features between the original images and the projection of the reference images are calculated by digital image correlation (DIC). Experiments indicate that our method obtains a comparable result with the multi-parameter model method using a large number of pictures, and contributes a 28.5% improvement to the reprojection error over the polynomial distortion model. MDPI 2022-05-05 /pmc/articles/PMC9105339/ /pubmed/35591215 http://dx.doi.org/10.3390/s22093524 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jin, Ziyi Li, Zhixue Gan, Tianyuan Fu, Zuoming Zhang, Chongan He, Zhongyu Zhang, Hong Wang, Peng Liu, Jiquan Ye, Xuesong A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition |
title | A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition |
title_full | A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition |
title_fullStr | A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition |
title_full_unstemmed | A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition |
title_short | A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition |
title_sort | novel central camera calibration method recording point-to-point distortion for vision-based human activity recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105339/ https://www.ncbi.nlm.nih.gov/pubmed/35591215 http://dx.doi.org/10.3390/s22093524 |
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