Abstract
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 ∼40% 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 CoastSnap sites, where each image corresponds to a different camera.
Highlights
IntroductionEngineers and scientists need coastal state information at small scales of days to weeks and meters to kilometers [1]
Coastal managers, engineers and scientists need coastal state information at small scales of days to weeks and meters to kilometers [1]
The proposed methodology was based on well-known feature detecting and matching algorithms and allows for massive automatic calibrations of an Argus camera provided a set, or basis, of calibrated images
Summary
Engineers and scientists need coastal state information at small scales of days to weeks and meters to kilometers [1]. Images have been used, in a quantitative way, to locate the shoreline and study its evolution [13,14,15], to determine the intertidal morphology [16,17,18], to estimate the wave period, celerity and propagation direction [19,20] and to infer bathymetries [21,22]. For these latter applications, in which magnitudes in physical space are required, the accurate georeferencing of images is essential [23,24]
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