Measuring Surface Velocity of Water Flow by Dense Optical Flow Method

Abstract

Recent advances in video processing technology have provided a new approach to measuring the surface velocity of water flow (SVWF). However, most of the previous researches using video processing technology depended on tracers for target tracing, requiring spraying tracers in the measurement process. These methods are not convenient for velocity measurement. In this study, a dense optical flow method (Farneback optical flow method) was used to process the water flow video to get the estimated SVWFs. The estimated SVWFs were verified by the actual SVWFs measured by a portable propeller velocimeter. The regression analyses between the estimated SVWFs and the measured SVWFs were conducted. The coefficient of determinations (R2) of the estimated and the measured SVWFs in different test regions are between 0.81 and 0.85. The average relative errors of the estimated and the measured SVWFs in all test regions are no more than 6.5%. The results indicate that the method had a good accuracy in estimating the SVWF and is a feasible and promising approach to analyzing the surface velocity distribution of water flow.

Convolutional Neural Network and Optical Flow for the Assessment of Wave and Tide Parameters from Video Analysis (LEUCOTEA): An Innovative Tool for Coastal Monitoring

“…Optical Flow techniques have been used to determine wave setting in the proximity of the coast. In particular, wave flow has been determined by detecting the pixel intensity of the wave front recorded in the videos [26,27]. The conversion of the pixel width into spatial scales, using topographic data detected with a Laser scanner, allowed for evaluating the wave flow vectors.…”

“…The wave features were assessed through a dense Optical Flow module in a MATLAB environment, with the same approach followed by Wu et al [26]. The Optical Flow assessment is based on the object motion estimation observed between consecutive sequence frames caused by the relative movement between the object and camera.…”

“…Foam is the predominant target traced within the surf zone through Optical Flow [3,25]. Moreover, optical images will also sense light reflections from sloped water surfaces and turbidity that alters background water colors [26]. Furthermore, alongshore currents have been estimated by extracting alongshore pixel time stacks from video [27,28].…”

Comparison of dense optical flow and PIV techniques for mapping surface current flow in tidal stream energy sites

“…MATLAB offers built-in methods for dense optical flow of which there are three algorithms available. Equations for the Gunnar-Farneback dense optical flow algorithm are explained elsewhere [33,34,43]. A MATLAB script was written which reads in the video, performs image pre-processing and dense optical flow.…”

Optical-Flow based Analysis for Range Hoods captured Flow Measurement

“…In literature, several researches are found as validation of the technique through the comparison with the PIV, such as Corpetti et al [6] and Liu et al [7]. Among all the computer-vision methods, the optical-flow technique is one of the mostly employed approach in this field [8,9,10], but in general, it has been widely applied to research displacement fields in solid bodies [11,12,13,14], liquid flows [15] and gas flows [16,17,18]. Optical-flow is related to the motion of visualfeatures, such as corners, edges, ridges and textures in two consecutive frames of a video scene [19,20].…”