Calibration of oblique image-based monitoring systems using photogrammetric principles

Autonomous camera-based systems combined with image velocimetry analyses, enables operational river flow measurements in rapidly responding rivers. The open-source OpenRiverCam (ORC) software stack supports edge and cloud video processing, time-series generation, and rating-curve development, enabling fully operational, scalable non-contact water level and discharge estimation with relatively affordable camera systems.

Despite these advances, image calibration remains a major bottleneck for broad uptake, as it typically requires high-precision surveying of non-collinear ground control points to constrain the camera's pose. This process is often complex and relies on instruments that are not readily available to many users.

We investigate a photogrammetry-based alternative workflow for camera pose estimation for possible integration in ORC: during camera installation, users collect a set of smartphone photographs from multiple viewpoints near the camera location. A photogrammetric reconstruction using these photos together with a sample video from the installed camera, jointly estimates the camera pose and lens parameters. The resulting camera pose is then used to orthorectify videos in operational data collection. Using controlled experiments and field experiments in New Zealand, Zambia and The Netherlands, we assess here (i) the accuracy of reconstructed 3D coordinates compared to traditional calibration, (ii) methods to robustly constrain the horizontal plane, (iii) the number of photographs required, and (iv) the influence of GPS accuracy on the solution.

This approach aims to significantly simplify calibration workflows and lower the barrier to deploying camera-based river monitoring systems.