Surface water velocity measurement using video processing: A survey
08 Sep 2014-pp 1-5
TL;DR: The aim of this paper is to implement the noncontact velocity measurement system that is based on an image based technique, named Particle Image Velocimetry (PIV), and to provide a summary of previous work done in this area.
Abstract: Measurement of velocity has been a challenging task in the surface water studies. Several attempts to measure the turbulences with contact sensors could not yield accurate results as they alter the flow pattern. The aim of this paper is to implement the noncontact velocity measurement system that is based on an image based technique, named Particle Image Velocimetry (PIV). This paper provides a summary of previous work done in this area and also about the application domains in which noncontact sensors can be used. The process of measurement is initiated by taking video of the water surface using a digital camera which is fixed at a strategically chosen environment. The recorded images are transformed to their undistorted appearance and then processed to obtain velocities at the water surface. The movement of the flow is estimated from pair of consecutive frames through statistical inference conducted on the image patterns floating on the free surface. Velocities are then calculated over the entire image by dividing the estimated displacements by the time interval between successive frames. The main motive of this survey is to provide the summary of the methods observed till now to the researchers.
Citations
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01 Nov 2015
TL;DR: In this paper, the authors presented two types of real time water velocity measurement system, first contact type water velocity measurements and another is non-contact type water measurement system using video processing based optical flow technique.
Abstract: In this paper we present two type of real time water velocity measurement system, first contact type water velocity measurement and another is non-contact type water velocity measurement system. In contact type, Hall Effect water flow sensor is used as a sensing unit, as water flows through that sensor, it gives corresponding pulse signal, By counting number of pulses from the output of the sensor, it will be calibrated using Arduinouno board to find the water velocity. Non-contact type velocity measurement system is based on video processing based optical flow technique. The optical flow is estimated from consecutive frames from which displacement is calculated through sparse optical flow algorithm such as pyramidical Lucas-Kanade approach. The results for Non-contact type technique shown in the paper are calculated using CMOS camera on thecanal near Khadkwasla Damp at Pune.
8 citations
Cites background or methods from "Surface water velocity measurement ..."
...Finally the binary images are eroded to suppress the particles composed of only few pixels [3] [4] Feature Extraction is an essential part of this system....
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...When both the time and distance are calibrated, the velocities also have the real values[3] [1]....
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22 Dec 2014
TL;DR: This paper describes the flow visualization and surface water velocity measurement technique using the non-intrusive method which gives the instantaneous results and is reliable, flexible method which can be applied successfully in many engineering applications.
Abstract: The limitation with the method of calculating surface water velocity is the direct contact of the velocity measurement tool to the flowing water which results in the mechanical wear and high maintenance cost. This paper describes the flow visualization and surface water velocity measurement technique using the non-intrusive method. This system gives the instantaneous results and is reliable, flexible method which can be applied successfully in many engineering applications. It is a distension of a video imaging technique to measure the velocity using the pattern matching method. The system was indigenously developed with the help of CMOS camera and paper particles which were used as the tracer particles. If video is taken from far distance then ortho-rectification is applied to calculate accurate results. Video was recorded at Bombay airport model, CWPRS, Pune under different environmental conditions. The code is written in MATLAB which includes segmentation, pattern matching and post processing steps.
2 citations
01 Dec 2019
TL;DR: A complete monitoring system where crowd-sourced environmental measurements are harmonised and integrated as complementary to the in-situ monitoring system, installed at the Kifisos basin, in the process of developing improved flood models.
Abstract: Flood risk prediction requires consistent and accurate sensor measurements, usually provided from traditional in-situ environmental monitoring systems. Crowd-sourced data can complement these official data sources, allowing authorities to improve and fill gaps in the hazard assessment process. However, collecting this information from volunteers, with no technical knowledge and while using low-cost equipment such their smart phones and tablets, raises the question of quality and consistency. To alleviate this barrier two mobile applications were developed in the context of H2020 Scent project (grant agreement No. 688930). The Scent Explore guides volunteers to areas of interests and supports them in the collection of video and images. These multimedia are processed in the back-end, image recognition techniques extract the water level from images containing a measuring tape and video processing algorithms extract the water surface velocity from video containing a predefined floating object moving on the surface of a water body, to extract river measurements as needed. The Scent Measure communicates with the portable sensors available at the area of interest and records the air temperature and the soil moisture. We present here a complete monitoring system where crowd-sourced environmental measurements are harmonised and integrated as complementary to the in-situ monitoring system, installed at the Kifisos basin, in the process of developing improved flood models.
1 citations
Cites background from "Surface water velocity measurement ..."
...Volunteers are asked to capture video of a pre-defined floating object (a tennis ball) as it passes in front of them following the river course [7]....
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01 Jan 2010
TL;DR: In this article, a real-time two-dimensional correlation speed measurement was proposed on image processing using high speed CMOS shown in figure 1, a cardboard with images which was fixed in electric control movement device was moving to simulate moving object, the CMOS image sensor gathered image signals of the cardboard and the image signals was output to FPGA after a serials pretreatment such as binaryzation etc.
Abstract: In order to implement two-dimensional moving object's non-contact and real-time speed measurement, in this paper, we proposed a new method which was based on the characteristic of FPGA and a real-time two-dimensional correlation speed measurement was proposed on image processing using high speed CMOS shown in figure 1.Experimental installation was shown in figure 2,a cardboard with images which was fixed in electric control movement device was moving to simulate moving object, the CMOS image sensor gathered image signals of the cardboard and the image signals was output to FPGA after a serials pretreatment such as binaryzation etc. Logical operation insteaded mathematical operation on FPGA to implement two frames image's cross-correlation operations as the formula (10) in this paper, and finally calculate the two-dimensional velocity. Through experiment result of table 1 we drawn an conclusion the accurarcy of the method in this paper was less than 1 pixel and the method can be used at a certain speed ranges of two-dimensional, real-time speed measurement.
References
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TL;DR: Large-scale particle image velocimetry (LSPIV) is a nonintrusive approach to measure velocities at the free surface of a water body as mentioned in this paper.
Abstract: [1] Large-scale particle image velocimetry (LSPIV) is a nonintrusive approach to measure velocities at the free surface of a water body. The raw LSPIV results are instantaneous water surface velocity fields, spanning flow areas up to hundreds of square meters. Measurements conducted in typical conditions in conjunction with appropriate selections of parameters for image processing resulted in mean velocity errors of less than 3.5%. The current article reviews the background of LSPIV and the work of three research teams spanning over a decade. Implementation examples using various LSPIV configurations are then described to illustrate the capability of the technique to characterize spatially distributed two- and three-dimensional flow kinematic features that can be related to important morphologic and hydrodynamic aspects of natural rivers. Finally, results and a critique of research methods are discussed to encourage LSPIV use and to improve its capabilities to collect field data needed to better understand complex geomorphic, hydrologic, and ecologic river processes and interactions under normal and extreme conditions.
306 citations
"Surface water velocity measurement ..." refers background in this paper
...Relationship between the Camera and the Field Coordinate Systems [5]...
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...The particle size, density composition, and concentration are important factors when selecting tracer for LSPIV [2], [5]....
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...For accurate result orthographic projection is needed[5]....
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TL;DR: In this paper, measurements acquired with Large-Scale Particle Image Velocimetry (LSPIV) during normal flows and floods in the Iowa River were compared to the rating curve extrapolated for high flows using a one-to-one discharge-stage relationship.
90 citations
"Surface water velocity measurement ..." refers methods in this paper
...When the couple mirrors are used to make the circular shift of particle it gives the moderate accuracy [9]....
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...[9] Flow field tracer particle image in adjacent timer are obtained and the space related theory or digital image analysis technique is used to get particle displacement and then get velocity distribution....
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26 Sep 2006
TL;DR: In this paper, an algorithm for particle tracking velocimetry working with the individual identification of seeding particles in a flow is developed, which is able to find particle pairs by the intensity of the light scattered and the size and shape of the particles.
Abstract: An algorithm for velocimetry working with the individual identification of seeding particles in a flow is developed in this paper. The algorithm is able to find particle pairs by the intensity of the light scattered and the size and shape of the particles. The velocity field of fluids and sediment particles of irregular size and shape and higher density than the fluid were experimentally measured. An experimental flume and a Particle Tracking Velocimetry (PTV) system provided with a double pulsed laser and high speed CCD camera was used. For image analysis an algorithm presented in this work was able to give sediment velocities for different particle ranges and proved to be a useful and efficient tool for PTV analysis of flows seed with non-spherical and non-uniform tracers.
20 citations
"Surface water velocity measurement ..." refers background or methods in this paper
...The window pair having the maximum cross-correlation value is the pattern's most probable displacement between two consecutive frames [2]....
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...The particle size, density composition, and concentration are important factors when selecting tracer for LSPIV [2], [5]....
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...On the other hand, the technique of Particle Tracking Velocimetry (PTV) [2], which can also be a very powerful tool, can also be used....
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...Cross-correlation Autocorrelation is a single frame double exposure method in which each illuminated image is exposed twice and it is difficult to furnish the temporal information, hence there is an directional ambiguity [2]....
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27 May 2008
TL;DR: A camera calibration method using circular control points where the error component of calibration is reduced compared with uncorrected points and the mathematical formulations of the correction terms are discussed.
Abstract: The objective of camera calibration is to establish the relationship between 3D world coordinates and their corresponding 2D image coordinates. There are various kinds of control points used in the calibration procedure. However, many of them are too sensitive to the thresholding error. Circular control points do not have this problem but they need to be corrected the distortion caused by the asymmetric perspective projection. In this paper, we present a camera calibration method using circular control points. The perspective transformation matrix is first estimated using DLT method. The centers of circle in the image plane are then corrected with the elements of perspective transformation matrix that will be recomputed after correction. After several iterations the remaining error will be small. The mathematical formulations of the correction terms are discussed. Real data has been used to test the proposed technique. The results indicate that the error component of calibration is reduced compared with uncorrected points.
13 citations
TL;DR: The new approach to the three-dimensional measurement of position and velocity of moving particles is introduced and applied to the measurement of the 3-D water-flow field seeded with tracer particles in a test tank and obtained satisfactory results.
Abstract: A new approach to the three-dimensional (3-D) measurement of position and velocity of moving particles is introduced. A single TV camera with an apparatus to add circular shift to the image enables us to record the 3-D movement of particles as spiral streaks on a single image. Every shape of the spiral streak on the image plane is related to the position and the velocity of the individual particle. The information about 3-D movement of particles is extracted from the image using an image processing technique. We applied the technique to the measurement of the 3-D water-flow field seeded with tracer particles in a test tank and obtained satisfactory results.
9 citations