E3S Web Conf.
Volume 40, 2018River Flow 2018 - Ninth International Conference on Fluvial Hydraulics
|Number of page(s)||8|
|Section||Fluid mechanics and sediment processes|
|Published online||05 September 2018|
Feature Tracking Velocimetry Applied to Airborne Measurement Data from Murg Creek
State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, 100084, Beijing, China
2 Laboratory of Hydraulics, Hydrology and Glaciology VAW, ETH Zurich, 8093, Zurich, Switzerland
* Corresponding author: firstname.lastname@example.org
A new image feature tracking velocimetry is presented and tested on airborne video data available from a previous study at Murg Creek (Canton Thurgau, Switzerland). Here, the seeded flow scenery had been recorded by an off-the-shelf action camera mounted to a low-cost quadcopter, and video frames were ortho-rectified to sizes of 4482×2240 px2 at a scale of 64 px/m. The new velocimetry approach is as follows: An adaptive Gaussian mixture model is used for video background subtraction. Then, scale-invariant keypoints on each remaining binary foreground image frame are determined by a feature detection algorithm, and corresponding feature points in subsequent frame pairs are matched using the iterative random sample consensus method. The related feature shifts in metric space divided by the video frame rate finally give the velocity vectors. The obtained velocimetry fields are compared with findings from both a particle image velocimetry and particle tracking velocimetry analysis in terms of accuracy and needed computational power. Indication is given that the feature tracking algorithm presents slightly less precise results, but clearly outperforms the other two in relation to computational power. Therefore, the new simplified method provides a high potential tool that may enable a future way to real time surface velocity measurements obtained from unmanned airborne vehicles.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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