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• W2891286885 abstract "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 px 2 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." @default.
• W2891286885 created "2018-09-27" @default.
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• W2891286885 date "2018-01-01" @default.
• W2891286885 modified "2023-09-25" @default.
• W2891286885 title "Feature Tracking Velocimetry Applied to Airborne Measurement Data from Murg Creek" @default.
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• W2891286885 doi "https://doi.org/10.1051/e3sconf/20184005030" @default.
• W2891286885 hasPublicationYear "2018" @default.
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