River flow estimation using video data

Bacharidis, Konstantinos; Moirogiorgou, Konstantia; Sibetheros, Ioannis A.; Savakis, Andreas; Zervakis, Michalis

doi:10.1109/ist.2014.6958468

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Different from filtered PTV and PTV-Stream, OTV allowed for generating a much more significant number of trajectories, which is beneficial to estimate the uncertainty of velocity estimations, and it also required less input since the detection and tracking is fully automated. Another advantage with respect to traditional PTV is the independence of OTV on the shape and size of the tracers that allows for improved performance in case of naturally occurring floaters [34,39]. Compared to LSPIV and traditional unfiltered PTV, OTV led to more robust velocity estimations with lower standard deviations.…”

Section: Comparison To Alternative Velocimetry Algorithmsmentioning

confidence: 99%

“…Both global and local optical flow approaches have been demonstrated for fluid flow monitoring, and in several laboratory applications these methodologies have been adopted to estimate the velocity field of fluid flows [37,38]. Regarding optical flow in environmental settings, in [39], an altered version of the global approach by [40] has been applied to extract the surface flow velocity field of a river from two video frames. Similarly, in [41], the velocity field of a dam break flow and of an aerated stepped spillway flow has been estimated.…”

Section: Introductionmentioning

confidence: 99%

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Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

et al. 2018

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Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7-8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

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Section: Comparison To Alternative Velocimetry Algorithmsmentioning

confidence: 99%