UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems
Following the path set out by the “Argus” project, video monitoring stations have become a very popular low cost tool to continuously monitor beaches around the world. For these stations to be able to offer quantitative results, the cameras must be calibrated. Cameras are typically calibrated when i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/14/2795 |
| _version_ | 1797526136405622784 |
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| author | Gonzalo Simarro Daniel Calvete Paola Souto |
| author_facet | Gonzalo Simarro Daniel Calvete Paola Souto |
| author_sort | Gonzalo Simarro |
| collection | DOAJ |
| description | Following the path set out by the “Argus” project, video monitoring stations have become a very popular low cost tool to continuously monitor beaches around the world. For these stations to be able to offer quantitative results, the cameras must be calibrated. Cameras are typically calibrated when installed, and, at best, extrinsic calibrations are performed from time to time. However, intra-day variations of camera calibration parameters due to thermal factors, or other kinds of uncontrolled movements, have been shown to introduce significant errors when transforming the pixels to real world coordinates. Departing from well-known feature detection and matching algorithms from computer vision, this paper presents a methodology to automatically calibrate cameras, in the intra-day time scale, from a small number of manually calibrated images. For the three cameras analyzed here, the proposed methodology allows for automatic calibration of >90% of the images in favorable conditions (images with many fixed features) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>∼</mo><mn>40</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the worst conditioned camera (almost featureless images). The results can be improved by increasing the number of manually calibrated images. Further, the procedure provides the user with two values that allow for the assessment of the expected quality of each automatic calibration. The proposed methodology, here applied to Argus-like stations, is applicable e.g., in <i>CoastSnap</i> sites, where each image corresponds to a different camera. |
| first_indexed | 2024-03-10T09:24:56Z |
| format | Article |
| id | doaj.art-a4fc27e698434beeac46bb6812ab5c92 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:24:56Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a4fc27e698434beeac46bb6812ab5c922023-11-22T04:52:31ZengMDPI AGRemote Sensing2072-42922021-07-011314279510.3390/rs13142795UCalib: Cameras Autocalibration on Coastal Video Monitoring SystemsGonzalo Simarro0Daniel Calvete1Paola Souto2ICM (CSIC), Passeig Marítim de la Barceloneta 37-49, 08003 Barcelona, SpainDepartament de Física, Universitat Politècnica de Catalunya, Jordi Girona 1-3, 08034 Barcelona, SpainDipartimento di Fisica e Scienze della Terra, Università de Ferrara, 44122 Ferrara, ItalyFollowing the path set out by the “Argus” project, video monitoring stations have become a very popular low cost tool to continuously monitor beaches around the world. For these stations to be able to offer quantitative results, the cameras must be calibrated. Cameras are typically calibrated when installed, and, at best, extrinsic calibrations are performed from time to time. However, intra-day variations of camera calibration parameters due to thermal factors, or other kinds of uncontrolled movements, have been shown to introduce significant errors when transforming the pixels to real world coordinates. Departing from well-known feature detection and matching algorithms from computer vision, this paper presents a methodology to automatically calibrate cameras, in the intra-day time scale, from a small number of manually calibrated images. For the three cameras analyzed here, the proposed methodology allows for automatic calibration of >90% of the images in favorable conditions (images with many fixed features) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>∼</mo><mn>40</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the worst conditioned camera (almost featureless images). The results can be improved by increasing the number of manually calibrated images. Further, the procedure provides the user with two values that allow for the assessment of the expected quality of each automatic calibration. The proposed methodology, here applied to Argus-like stations, is applicable e.g., in <i>CoastSnap</i> sites, where each image corresponds to a different camera.https://www.mdpi.com/2072-4292/13/14/2795video monitoring stations for beachesvideo stabilizationfeature detection and matching algorithms |
| spellingShingle | Gonzalo Simarro Daniel Calvete Paola Souto UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems Remote Sensing video monitoring stations for beaches video stabilization feature detection and matching algorithms |
| title | UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems |
| title_full | UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems |
| title_fullStr | UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems |
| title_full_unstemmed | UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems |
| title_short | UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems |
| title_sort | ucalib cameras autocalibration on coastal video monitoring systems |
| topic | video monitoring stations for beaches video stabilization feature detection and matching algorithms |
| url | https://www.mdpi.com/2072-4292/13/14/2795 |
| work_keys_str_mv | AT gonzalosimarro ucalibcamerasautocalibrationoncoastalvideomonitoringsystems AT danielcalvete ucalibcamerasautocalibrationoncoastalvideomonitoringsystems AT paolasouto ucalibcamerasautocalibrationoncoastalvideomonitoringsystems |