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Optical Flow For Image-Based River Velocity Estimation

Musaab Khalid 1, * Lionel Pénard 1 Etienne Mémin 2 
* Corresponding author
2 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
IRMAR - Institut de Recherche Mathématique de Rennes, IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, Inria Rennes – Bretagne Atlantique
Abstract : We present a novel motion estimation technique for image-based river velocimetry. It is based on the so-called optical flow, which is a well developed method for rigid motion estimation in image sequences, devised in computer vision community. Contrary to PIV (Particle Image Velocimetry) techniques, optical flow formulation is flexible enough to incorporate physics equations that govern the observed quantity motion. Over the past years, it has been adopted by experimental fluid dynamics community where many new models were introduced to better represent different fluids motions, (see (Heitz et al., 2010) for a review). Our optical flow is based on the scalar transport equation and is augmented with a weighted diffusion term to compensate for small scale (non-captured) contributions. Additionally, since there is no ground truth data for such type of image sequences, we present a new evaluation method to assess the results. It is based on trajectory reconstruction of few Lagrangian particles of interest and a direct comparison against their manually-reconstructed trajectories. The new motion estimation technique outperformed traditional optical flow and PIV-based methods.
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Submitted on : Thursday, November 22, 2018 - 9:27:36 AM
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Musaab Khalid, Lionel Pénard, Etienne Mémin. Optical Flow For Image-Based River Velocity Estimation. Flow Measurement and Instrumentation, Elsevier, 2019, 65, pp.110-121. ⟨10.1016/j.flowmeasinst.2018.11.009⟩. ⟨hal-01930498⟩



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