{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:52:08Z","timestamp":1775609528428,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007707","name":"INAIL (National Institute for Insurance against Accidents)","doi-asserted-by":"publisher","award":["BRIC2016-ID24"],"award-info":[{"award-number":["BRIC2016-ID24"]}],"id":[{"id":"10.13039\/501100007707","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Calibrating intrinsic and extrinsic camera parameters is a fundamental problem that is a preliminary task for a wide variety of applications, from robotics to computer vision to surveillance and industrial tasks. With the advent of Internet of Things (IoT) technology and edge computing capabilities, the ability to track motion activities in large outdoor areas has become feasible. The proposed work presents a network of IoT camera nodes and a dissertation on two possible approaches for automatically estimating their poses. One approach follows the Structure from Motion (SfM) pipeline, while the other is marker-based. Both methods exploit the correspondence of features detected by cameras on synchronized frames. A preliminary indoor experiment was conducted to assess the performance of the two methods compared to ground truth measurements, employing a commercial tracking system of millimetric precision. Outdoor experiments directly compared the two approaches on a larger setup. The results show that the proposed SfM pipeline more accurately estimates the pose of the cameras. In addition, in the indoor setup, the same methods were used for a tracking application to show a practical use case.<\/jats:p>","DOI":"10.3390\/jsan11030040","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T02:06:42Z","timestamp":1659319602000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-Camera Extrinsic Calibration for Real-Time Tracking in Large Outdoor Environments"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3225-2782","authenticated-orcid":false,"given":"Paolo","family":"Tripicchio","sequence":"first","affiliation":[{"name":"Mechanical Intelligence Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant\u2019Anna, 56100 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7065-8789","authenticated-orcid":false,"given":"Salvatore","family":"D\u2019Avella","sequence":"additional","affiliation":[{"name":"Mechanical Intelligence Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant\u2019Anna, 56100 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9850-2524","authenticated-orcid":false,"given":"Gerardo","family":"Camacho-Gonzalez","sequence":"additional","affiliation":[{"name":"Mechanical Intelligence Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant\u2019Anna, 56100 Pisa, Italy"}]},{"given":"Lorenzo","family":"Landolfi","sequence":"additional","affiliation":[{"name":"Istituto Italiano di Tecnologia (IIT), 16163 Genoa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1721-9055","authenticated-orcid":false,"given":"Gabriele","family":"Baris","sequence":"additional","affiliation":[{"name":"Mechanical Intelligence Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant\u2019Anna, 56100 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5802-541X","authenticated-orcid":false,"given":"Carlo Alberto","family":"Avizzano","sequence":"additional","affiliation":[{"name":"Mechanical Intelligence Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant\u2019Anna, 56100 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6078-6429","authenticated-orcid":false,"given":"Alessandro","family":"Filippeschi","sequence":"additional","affiliation":[{"name":"Mechanical Intelligence Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant\u2019Anna, 56100 Pisa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101888","DOI":"10.1016\/j.rcim.2019.101888","article-title":"A study on picking objects in cluttered environments: Exploiting depth features for a custom low-cost universal jamming gripper","volume":"63","author":"Tripicchio","year":"2020","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1007\/s00170-020-06146-4","article-title":"Welding defect detection: Coping with artifacts in the production line","volume":"111","author":"Tripicchio","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Filippeschi, A., Pellicci, M., Vanni, F., Forte, G., Bassani, G., Landolfi, L., De Merich, D., Campo, G., Avizzano, C.A., and Bergamasco, M. (2019). The Sailport Project: A Trilateral Approach to the Improvement of Workers\u2019 Safety and Health in Ports. Advances in Safety Management and Human Factors, Proceedings of the International Conference on Applied Human Factors and Ergonomics, Washington, DC, USA, 24\u201328 July 2019, Springer.","DOI":"10.1007\/978-3-030-20497-6_7"},{"key":"ref_4","unstructured":"Zhang, Z. (1999, January 20\u201327). Flexible camera calibration by viewing a plane from unknown orientations. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1023\/A:1007982716991","article-title":"Self-calibration of a moving camera from point correspondences and fundamental matrices","volume":"22","author":"Luong","year":"1997","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2280","DOI":"10.1016\/j.patcog.2014.01.005","article-title":"Automatic Generation and Detection of Highly Reliable Fiducial Markers Under Occlusion","volume":"47","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Olson, E. (2011, January 9\u201313). AprilTag: A robust and flexible visual fiducial system. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979561"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sagitov, A., Shabalina, K., Sabirova, L., Li, H., and Magid, E. (2017, January 26\u201328). ARTag, AprilTag and CALTag Fiducial Marker Systems: Comparison in a Presence of Partial Marker Occlusion and Rotation. Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Madrid, Spain.","DOI":"10.5220\/0006478901820191"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Daftry, S., Maurer, M., Wendel, A., and Bischof, H. (2013, January 9\u201313). Flexible and User-Centric Camera Calibration using Planar Fiducial Markers. Proceedings of the British Machine Vision Conference, Bristol, UK.","DOI":"10.5244\/C.27.19"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MRA.2011.943233","article-title":"Visual odometry [tutorial]","volume":"18","author":"Scaramuzza","year":"2011","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s40903-015-0032-7","article-title":"An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics","volume":"1","author":"Yousif","year":"2015","journal-title":"Intell. Ind. Syst."},{"key":"ref_12","unstructured":"Tripicchio, P., Unetti, M., Giordani, N., Avizzano, C.A., and Satler, M. (2014, January 2\u20134). A lightweight slam algorithm for indoor autonomous navigation. Proceedings of the Australasian Conference on Robotics and Automation (ACRA 2014), Melbourne, Australia."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hartley, R.I., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kaneko, M., and Nakamura, Y. (2011). Large-Scale Visual Odometry for Rough Terrain. Robotics Research: The 13th International Symposium ISRR, Springer.","DOI":"10.1007\/978-3-642-14743-2"},{"key":"ref_15","unstructured":"Corke, P., Strelow, D., and Singh, S. (October, January 28). Omnidirectional visual odometry for a planetary rover. Proceedings of the 2004 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), Sendai, Japan."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Forster, C., Pizzoli, M., and Scaramuzza, D. (June, January 31). SVO: Fast semi-direct monocular visual odometry. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6906584"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yi, G., Jianxin, L., Hangping, Q., and Bo, W. (2014, January 13\u201314). Survey of structure from motion. Proceedings of the 2014 International Conference on Cloud Computing and Internet of Things, Changchun, China.","DOI":"10.1109\/CCIOT.2014.7062508"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Korthals, T., Wolf, D., Rudolph, D., Hesse, M., and R\u00fcckert, U. (2019, January 4\u20136). Fiducial Marker based Extrinsic Camera Calibration for a Robot Benchmarking Platform. Proceedings of the 2019 European Conference on Mobile Robots (ECMR), Prague, Czech Republic.","DOI":"10.1109\/ECMR.2019.8870969"},{"key":"ref_19","unstructured":"Harris, C., and Stephens, M. (September, January 31). A combined corner and edge detector. Proceedings of the Alvey Vision Conference, Manchester, UK."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2006). Machine learning for high-speed corner detection. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/11744023_34"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jakubovi\u0107, A., and Velagi\u0107, J. (2018, January 16\u201319). Image feature matching and object detection using brute-force matchers. Proceedings of the 2018 International Symposium ELMAR, Zadar, Croatia.","DOI":"10.23919\/ELMAR.2018.8534641"},{"key":"ref_24","unstructured":"Muja, M., and Lowe, D. (2009). Flann-Fast Library for Approximate Nearest Neighbors User Manual, Computer Science Department, University of British Columbia."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). Superpoint: Self-supervised interest point detection and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_26","unstructured":"Yang, T.Y., Nguyen, D.K., Heijnen, H., and Balntas, V. (2020). Ur2kid: Unifying retrieval, keypoint detection, and keypoint description without local correspondence supervision. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., and Sattler, T. (2019, January 15\u201320). D2-net: A trainable cnn for joint description and detection of local features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00828"},{"key":"ref_28","unstructured":"Li, H., and Hartley, R. (2006, January 20\u201324). Five-point motion estimation made easy. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TPAMI.2004.17","article-title":"An efficient solution to the five-point relative pose problem","volume":"26","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_31","first-page":"8755","article-title":"A practical method for decomposition of the essential matrix","volume":"8","author":"Georgiev","year":"2014","journal-title":"Appl. Math. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Triggs, B., McLauchlan, P.F., Hartley, R.I., and Fitzgibbon, A.W. (1999, January 21\u201322). Bundle adjustment\u2014A modern synthesis. Proceedings of the International Workshop on Vision Algorithms, Corfu, Greece.","DOI":"10.1007\/3-540-44480-7_21"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Balntas, V., Li, S., and Prisacariu, V. (2018, January 8\u201314). Relocnet: Continuous metric learning relocalisation using neural nets. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_46"},{"key":"ref_34","unstructured":"Ding, M., Wang, Z., Sun, J., Shi, J., and Luo, P. (November, January 27). CamNet: Coarse-to-fine retrieval for camera re-localization. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kendall, A., Grimes, M., and Cipolla, R. (2015, January 11\u201318). Posenet: A convolutional network for real-time 6-dof camera relocalization. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.336"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_37","first-page":"405","article-title":"The interpretation of structure from motion","volume":"203","author":"Ullman","year":"1979","journal-title":"Proc. R. Soc. Lond. Ser. B Biol. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Melekhov, I., Ylioinas, J., Kannala, J., and Rahtu, E. (2017, January 10\u201314). Relative camera pose estimation using convolutional neural networks. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Auckland, New Zealand.","DOI":"10.1007\/978-3-319-70353-4_57"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., and Cremers, D. (2017, January 22\u201329). Image-based localization using lstms for structured feature correlation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.75"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kendall, A., and Cipolla, R. (2017, January 21\u201326). Geometric loss functions for camera pose regression with deep learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.694"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovic, R., and Sivic, J. (2017, January 21\u201326). Convolutional neural network architecture for geometric matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.12"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yi, K.M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., and Fua, P. (2018, January 18\u201322). Learning to find good correspondences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00282"},{"key":"ref_43","unstructured":"Shavit, Y., and Ferens, R. (2019). Introduction to camera pose estimation with deep learning. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, Y.J., Weng, X., and Kitani, K. (2021, January 20\u201325). Wide-Baseline Multi-Camera Calibration using Person Re-Identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01293"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/BF00127818","article-title":"The fundamental matrix: Theory, algorithms, and stability analysis","volume":"17","author":"Luong","year":"1996","journal-title":"Int. J. Comput. Vis."},{"key":"ref_46","unstructured":"Nister, D. (2003, January 16\u201322). An efficient solution to the five-point relative pose problem. Proceedings of the Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lourakis, M.L.A., and Argyros, A.A. (2005, January 17\u201321). Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment?. Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV\u201905), Beijing, China.","DOI":"10.1109\/ICCV.2005.128"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/JRA.1987.1087109","article-title":"A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses","volume":"3","author":"Tsai","year":"1987","journal-title":"IEEE J. Robot. Autom."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1051\/ro\/2010021","article-title":"Combining Odometry and Visual Loop-Closure Detection for Consistent Topo-Metrical Mapping","volume":"44","author":"Bazeille","year":"2011","journal-title":"RAIRO\u2014Oper. Res."},{"key":"ref_50","unstructured":"Fette, I., and Melnikov, A. (2022, June 12). The WebSocket Protocol. Available online: http:\/\/www.rfc-editor.org\/rfc\/rfc6455.txt."},{"key":"ref_51","unstructured":"Dahl, R. (2022, June 12). Node. js: Evented I\/O for v8 Javascript. Available online: https:\/\/www.nodejs.org."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/11\/3\/40\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:58:58Z","timestamp":1760140738000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/11\/3\/40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,29]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["jsan11030040"],"URL":"https:\/\/doi.org\/10.3390\/jsan11030040","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,29]]}}}