| Summary: | 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.
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