PTV-Stream: A simplified particle tracking velocimetry framework for stream surface flow monitoring

Tauro, Flavia; Piscopia, Rodolfo; Grimaldi, Salvatore

doi:10.1016/j.catena.2018.09.009

Cited by 49 publications

(32 citation statements)

References 37 publications

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“…In experiments on the Brenta River, a frame acquisition frequency lower than 7-8 Hz led to a sharp decrease in velocity estimation. Similar findings have also been observed in PTV-Stream implementations [19]. Of course, the selection of the frame acquisition frequency is related to the size of the field of view and to flow velocity.…”

Section: Suitability Of Otv For Streamflow Observationssupporting

confidence: 80%

“…PTV analyses were, in turn, applied both without ("Unfiltered PTV") and with ("Filtered PTV") the trajectory-based filtering procedure. Details on the procedures and implementation of LSPIV and PTV can be found in [18], while the PTV-Stream approach as well as its implementation on the Brenta and Tiber Rivers are presented in [19]. On the Brenta River, LSPIV was executed on sequences subsampled at 12.5 Hz and adopting the 1-2, 2-3 sequencing style.…”

Section: Alternative Algorithmsmentioning

confidence: 99%

“…Conversely, PTV combined with trajectory-based filtering has led to more accurate surface flow velocity estimations in both settings. Towards a more automated PTV approach, in [19], a novel nearest-neighbor PTV approach has been introduced (PTV-Stream) that affords the identification and tracking of features of any shape (rather than of round-shaped particles as in traditional correlation-based PTV). However, despite the versatility of PTV in scarcely seeded streams, both LSPIV and PTV are severely influenced by sunlight reflections and require a priori knowledge of several parameters, including tracer dimension and velocity, and the average surface flow velocity direction.…”

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: Suitability Of Otv For Streamflow Observationssupporting

confidence: 80%

Section: Alternative Algorithmsmentioning

confidence: 99%

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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“…PTV is a nonintrusive image-based approach used to trace the velocity of individual particles along trajectories and has been widely applied to determine the particle velocities in both laboratory and field settings [27][28][29][30][31][32][33]. It mainly consists of two steps: individual particle identification and particle tracking.…”

Section: Ptvmentioning

confidence: 99%

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

Niu

Liu

et al. 2019

Sensors

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A laboratory model test is an effective method for studying landslide risk mitigation. In this study, thermal infrared (TIR) imagery, a modern no-contact technique, was introduced and integrated with terrestrial laser scanning (TLS) and particle tracking velocimetry (PTV) to characterize the failure of a landslide model. The characteristics of the failure initiation, motion, and region of interest, including landslide volume, deformation, velocity, surface temperature changes, and anomalies, were detected using the integrated monitoring system. The laboratory test results indicate that the integrated monitoring system is expected to be useful for characterizing the failure of landslide models. The preliminary results of this study suggest that a change in the relative TIR signal (ΔTIR) can be a useful index for landslide detection, and a decrease in the average value of the temperature change ( Δ T I R ¯ ) can be selected as a precursor to landslide failure.

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“…It is designed for low density particle distribution and, in most implementations, requires that particles are round-shaped [36]. Some implementations, such as PTV-Stream [38], can deal with tracers of any shape, but they have other limitations, e.g., the necessity to know in advance the direction of the flow average velocity. This is challenging in heterogeneous flow conditions, where velocities in the sub-regions of the ROI can have opposite directions.…”

Section: Introductionmentioning

confidence: 99%

Drone-Based Optical Measurements of Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams

et al. 2020

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In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The efficiency of fish passages is being constantly monitored and improved. Since the likelihood of fish passages to be discovered by fish depends, inter alia, on flow conditions near their entrances, these conditions have to be monitored as well. In this study, we employ large-scale particle image velocimetry (LSPIV) in seeded flow conditions to analyse images of the area near a fish passage entrance, captured with the help of a ready-to-fly consumer drone. We apply LSPIV to short image sequences and test different LSPIV interrogation area sizes and correlation methods. The study demonstrates that LSPIV based on ensemble correlation yields velocities that are in good agreement with the reference values regarding both magnitude and flow direction. Therefore, this non-intrusive methodology has a potential to be used for flow monitoring near fish passages on a regular basis, enabling timely reaction to undesired changes in flow conditions when possible.

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PTV-Stream: A simplified particle tracking velocimetry framework for stream surface flow monitoring

Cited by 49 publications

References 37 publications

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

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

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

Drone-Based Optical Measurements of Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams

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