Motion estimation method under violent illumination variation based on corner matching and optic flow method

Abstract

A motion estimation method based on the angle-point matching and the optical flow method under violent light change, relating to the computer vision technology field, comprises the following steps of: first, carrying out angle-point detection to a present frame; next, carrying out normalized angle-point matching of the present frame and a last frame rotation-invariantly; then, dividing the matched frame into blocks and calculating the affine transformation parameter in each block, carrying out block global motion vector estimating of the present frame according to the affine transformation parameter and performing motion compensation of the last frame using the vector estimation; again, carrying out block linear illumination compensation of the last frame after the motion compensation; finally, estimating the global motion vector of the next frame via the optical flow computation of the linear illumination compensated image and the present image frame. With overcoming the advantages of the invalidation of the optical flow method caused by bad conditions, violent light change and great rotation, etc, the invention obtains higher accuracy.

Description

technical field [0001] The invention relates to a method in the technical field of image processing, in particular to a motion estimation method based on corner point matching and optical flow method under severe illumination changes. Background technique [0002] Optical flow method is an important method to analyze the motion of objects in sequence images, and has an extremely important application in video image analysis. When the object moves in front of the camera or the camera moves in the environment, the image will be changed. The speed distribution of the image mode movement is called optical flow, and each point on the image forms an optical flow field. However, due to the assumption of constant brightness between adjacent frames, its application range is greatly reduced. The problem of calculating the optical flow field mainly lies in two aspects: 1, the motion discontinuity caused by relative motion; 2, the drastic illumination change destroys the constant equat...

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Problems solved by technology

However, this model is relatively rough and has the following shortcomings: 1. Since the picture is an infrared picture, it is implicitly assumed that if the intensity of the illumination gain changes, then the gain of each pixel must change in the same way, and the change is uniform. is linear

But this is obviously inconsistent with the non-uniform change of image light intensity when the video surveillance cameras we usually use scan from bri

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