Fluid motion vector estimation method based on feature optical flow

Abstract

The invention discloses a fluid motion vector estimation method based on feature optical flow, comprising the following steps: first, carrying out feature point extraction and sub-pixel location on two consecutive frames of images I1 and I2 in an H.264 video stream under different resolutions through use of a multi-scale SUSAN feature point detection algorithm to get feature point sets P1 and P2; then, using an H.264 video compression domain priori feature optical flow estimation algorithm to extract the motion vector of a macro block where the feature points in P1 are located as priori information for setting the search radius, searching for matching feature points in the feature point set P2, estimating an optical flow motion vector, and getting a sparse feature optical flow field; and finally, meshing the sparse feature optical flow field through use of a flow field meshing algorithm based on inverse distance weighted interpolation to get a uniform two-dimensional flow velocity vector field. The method can be applied to motion vector estimation of natural tracer water such as river water surface, and is especially applicable to high-spatial-resolution two-dimensional instantaneous flow field measurement.

Description

technical field [0001] The invention relates to a method for estimating a fluid motion vector, in particular to a method for estimating a fluid motion vector based on characteristic optical flow, and belongs to the technical field of flow field measurement. Background technique [0002] There are a large number of fluid microclusters with no fixed mass and relative motion in the fluid, which makes the motion of the fluid more complex than that of rigid bodies and solids. In the past two decades, the development of optics, electronics and computer technology has promoted the realization and application of particle image velocimetry (PIV), a non-contact instantaneous full-field flow velocity measurement technology. It obtains the size, direction, characteristics and distribution of local fluid motion displacement and velocity through the analysis and calculation of particle image sequences, which greatly improves the measurement capabilities of various complex flows in the lab...

AI Technical Summary

Problems solved by technology

However, in the field of computer vision, feature points are mainly used for the recognition and classification of large objects such as people and vehicles. For the motion vector estimation of small objects, there are still insufficient detection and positioning accuracy.

For example, the gradient-based Harris operator extracts the local maximum point of the image gray gradient as the corner point, which has rotation invariance and high stability; but the anti-noise ability is poor, and the position of the corner point may shift or Pseudo corners

The grayscale-based SUSAN operator can extract the corners and edges of the target in strong noise, and the positioning accuracy reaches the pixel level; but it does not have scale invariance

The SIFT operator based on the multi-scale idea can extract the local feature points in the image with scale, rotation and affine invariance; but it often does not correspond to the real corner points on the target, because it needs to be extracted from the differential Gaussian (DoG) pyramid 128-dimensional feature vector, the amount of calculation is also considerable

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