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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.<\/jats:p>","DOI":"10.3390\/s22093524","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T02:46:39Z","timestamp":1651805199000},"page":"3524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1591-9736","authenticated-orcid":false,"given":"Ziyi","family":"Jin","sequence":"first","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixue","family":"Li","sequence":"additional","affiliation":[{"name":"Independent Researcher, 181 Gaojiao Road, Yuhang District, Hangzhou 311122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyuan","family":"Gan","sequence":"additional","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuoming","family":"Fu","sequence":"additional","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyu","family":"He","sequence":"additional","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiquan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3439-3733","authenticated-orcid":false,"given":"Xuesong","family":"Ye","sequence":"additional","affiliation":[{"name":"Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China"},{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y. (2017, January 21\u201326). Realtime multi-person 2d pose estimation using part affinity fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_2","first-page":"800","article-title":"2D human pose estimation based on object detection using RGB-D information","volume":"12","author":"Park","year":"2018","journal-title":"KSII Trans. Internet Inf. Syst. (TIIS)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1109\/TMM.2020.2965434","article-title":"Spatio-temporal attention networks for action recognition and detection","volume":"22","author":"Li","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.patrec.2014.04.011","article-title":"Human activity recognition from 3d data: A review","volume":"48","author":"Aggarwal","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106970","DOI":"10.1016\/j.knosys.2021.106970","article-title":"A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions","volume":"223","author":"Yadav","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/34.159901","article-title":"Camera calibration with distortion models and accuracy evaluation","volume":"14","author":"Weng","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","unstructured":"Claus, D., and Fitzgibbon, A.W. (2005, January 20\u201325). A rational function lens distortion model for general cameras. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TPAMI.2007.1147","article-title":"Parameter-free radial distortion correction with center of distortion estimation","volume":"29","author":"Hartley","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sagawa, R., Takatsuji, M., Echigo, T., and Yagi, Y. (2005, January 2\u20136). Calibration of lens distortion by structured-light scanning. Proceedings of the 2005 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada.","DOI":"10.1109\/IROS.2005.1545167"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.cviu.2009.05.005","article-title":"Efficient generic calibration method for general cameras with single centre of projection","volume":"114","author":"Dunne","year":"2010","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Schops, T., Larsson, V., Pollefeys, M., and Sattler, T. (2020, January 13\u201319). Why having 10,000 parameters in your camera model is better than twelve. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00261"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Brousseau, P.-A., and Roy, S. (2019, January 27\u201328). Calibration of axial fisheye cameras through generic virtual central models. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00414"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s10043-019-00496-5","article-title":"Using distortion correction to improve the precision of camera calibration","volume":"26","author":"Jin","year":"2019","journal-title":"Opt. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1364\/OL.38.001446","article-title":"Camera calibration with active phase target: Improvement on feature detection and optimization","volume":"38","author":"Huang","year":"2013","journal-title":"Opt. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1364\/AO.55.002346","article-title":"Method for out-of-focus camera calibration","volume":"55","author":"Bell","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105919","DOI":"10.1016\/j.optlaseng.2019.105919","article-title":"Camera calibration using synthetic random speckle pattern and digital image correlation","volume":"126","author":"Chen","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.optlaseng.2017.06.008","article-title":"Accuracy evaluation of optical distortion calibration by digital image correlation","volume":"98","author":"Gao","year":"2017","journal-title":"Opt. Lasers Eng."},{"key":"ref_19","unstructured":"Swaninathan, R., Grossberg, M.D., and Nayar, S.K. (2003, January 18\u201320). A perspective on distortions. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1108\/k.2001.30.9_10.1333.2","article-title":"Multiple view geometry in computer vision","volume":"30","author":"Andrew","year":"2001","journal-title":"Kybernetes"},{"key":"ref_21","unstructured":"Grossberg, M.D., and Nayar, S.K. (2001, January 7\u201314). A general imaging model and a method for finding its parameters. Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vancouver, BC, Canada."},{"key":"ref_22","unstructured":"Ramalingam, S., Sturm, P., and Lodha, S.K. (2005, January 20\u201325). Towards complete generic camera calibration. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dansereau, D.G., Pizarro, O., and Williams, S.B. (2013, January 23\u201328). Decoding, calibration and rectification for lenselet-based plenoptic cameras. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.137"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TPAMI.2016.2592904","article-title":"A unifying model for camera calibration","volume":"39","author":"Ramalingam","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","unstructured":"F\u00f6rstner, W., and G\u00fclch, E. (1987, January 2\u20134). A fast operator for detection and precise location of distinct points, corners and centres of circular features. Proceedings of the ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland."},{"key":"ref_26","unstructured":"Heikkila, J., and Silv\u00e9n, O. (1997, January 17\u201319). A four-step camera calibration procedure with implicit image correction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1109\/34.879788","article-title":"Geometric camera calibration using circular control points","volume":"22","author":"Heikkila","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"15269","DOI":"10.1364\/OE.25.015269","article-title":"Flexible and accurate camera calibration using grid spherical images","volume":"25","author":"Liu","year":"2017","journal-title":"Opt. Express"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27443","DOI":"10.1364\/OE.402826","article-title":"High-accuracy calibration of cameras without depth of field and target size limitations","volume":"28","author":"Yan","year":"2020","journal-title":"Opt. Express"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ha, H., Perdoch, M., Alismail, H., So Kweon, I., and Sheikh, Y. (2017, January 22\u201329). Deltille grids for geometric camera calibration. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.571"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"213427","DOI":"10.1117\/12.7972925","article-title":"Digital imaging techniques in experimental stress analysis","volume":"21","author":"Peters","year":"1982","journal-title":"Opt. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1111\/j.1475-1305.2005.00227.x","article-title":"An evaluation of digital image correlation criteria for strain mapping applications","volume":"41","author":"Tong","year":"2005","journal-title":"Strain"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1364\/AO.49.005501","article-title":"Equivalence of digital image correlation criteria for pattern matching","volume":"49","author":"Pan","year":"2010","journal-title":"Appl. Opt."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.optlaseng.2014.06.011","article-title":"Path-independent digital image correlation with high accuracy, speed and robustness","volume":"65","author":"Jiang","year":"2015","journal-title":"Opt. Lasers Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7674","DOI":"10.1364\/AO.51.007674","article-title":"Large deformation measurement using digital image correlation: A fully automated approach","volume":"51","author":"Zhou","year":"2012","journal-title":"Appl. Opt."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/BF02321405","article-title":"Digital image correlation using Newton-Raphson method of partial differential correction","volume":"29","author":"Bruck","year":"1989","journal-title":"Exp. Mech."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s11340-013-9717-6","article-title":"Fast, robust and accurate digital image correlation calculation without redundant computations","volume":"53","author":"Pan","year":"2013","journal-title":"Exp. Mech."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s11340-015-0009-1","article-title":"Ncorr: Open-source 2D digital image correlation matlab software","volume":"55","author":"Blaber","year":"2015","journal-title":"Exp. Mech."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1088\/0957-0233\/17\/6\/045","article-title":"Performance of sub-pixel registration algorithms in digital image correlation","volume":"17","author":"Bing","year":"2006","journal-title":"Meas. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.optlaseng.2015.01.012","article-title":"High accuracy digital image correlation powered by GPU-based parallel computing","volume":"69","author":"Zhang","year":"2015","journal-title":"Opt. Lasers Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3524\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:06:43Z","timestamp":1760137603000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,5]]},"references-count":40,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093524"],"URL":"https:\/\/doi.org\/10.3390\/s22093524","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,5]]}}}