Abstract
The recent technological advances in remote sensing (e.g., unmanned aerial vehicles, digital image acquisition, etc.) have vastly improved the applicability of image velocimetry in hydrological studies. Thus, image velocimetry has become an established technique with an acceptable error for practical applications (the error can be lower than 10%). The main source of errors has been attributed to incomplete intrinsic and extrinsic camera calibration, to non-constant frame rate and to spurious low velocities due to moving objects that are irrelevant to the streamflow. Some researchers have even employed probabilistic approaches (Monte Carlo simulations) to analyze the uncertainty introduced during the camera calibration procedure. On the other hand, the endogenous uncertainty of the image velocimetry algorithms per se has received little attention. In this study, a probabilistic approach is employed to systematically analyze this uncertainty. It is argued that this analysis may not only improve the performance of the image velocimetry methods but it can also provide information regarding the impact of the video recording conditions (e.g., low density of features, oblique camera angle, low resolution, etc.) on the accuracy of the estimated values. The suggested method has been tested in six case studies of which the data have been previously made publicly available by independent researchers.
Highlights
It has been more than forty years since the scientific field of computer vision expanded to include motion detection applications
The Monte Carlo simulations and how they were applied to study the impact of the uncertainty of the Free-large-scale particle image velocimetry (LSPIV) parameters are described
Free-LSPIV has been used [19,20], but the findings apply to any image velocimetry method that employs parameters that influence the sensitivity of the algorithm
Summary
It has been more than forty years since the scientific field of computer vision expanded to include motion detection applications. Motion detection is a ‘correspondence problem’, i.e., to identify the same distinct feature in two images at different times [1]. Fluid mechanics was one of the first fields to which motion detection was applied. In 1984, Adrian [2] employed a pulsed laser to measure, in laboratory conditions, 2D fluid velocity fields. He named this method particle image velocimetry (PIV). It took some time for image velocimetry to be applied in field conditions. Fujita et al [3] adapted the PIV method for measuring the velocity field in large water bodies and introduced the large-scale particle image velocimetry (LSPIV). Various other image velocimetry methods were subsequently suggested, such as the space-time image velocimetry (STIV) [4], the optical tracking velocimetry [5], the Kande–Lucas–Tomasi image velocimetry [6], etc
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
