Lagrange video motion amplification method based on image deformation technology

Abstract

The invention provides a Lagrange video motion amplification method based on an image deformation technology. The method comprises the steps: estimating the motion information of feature points of a region of interest in a video through an optical flow method, amplifying the motion displacement, and then taking the coordinates of the feature points before and after amplification as traction points, and achieving the motion amplification of an image through an image deformation technology for maintaining image content. Compared with the current motion amplification technology, the method has less blurring and ringing artifacts, and each frame of image can be processed only by a small amount of manual intervention. An input picture sequence is from a static camera; an amplification area canbe manually selected and amplification times can be set, the whole system can automatically detect and track feature points, a target area is deformed through an image deformation technology after motion is estimated again, and finally, a foreground and a background are reconstructed to obtain an amplified video sequence, so physical motion of a target in a video is amplified.

Description

technical field [0001] The invention relates to the application field of computer technology, in particular to a Lagrangian video motion amplification method based on image deformation technology. Background technique [0002] In the real world, every moment there are slight movements that are difficult for the human eye to detect. Video micro-motion amplification technology can display these motions. This technology is widely used in the fields of medical health, scientific analysis and building monitoring. Therefore, it is necessary to use motion amplification technology to solve the problem of difficult-to-detect micro-motion observation. However, this is a very difficult task because the motion in the original video is so faint that it is difficult to distinguish them from noise. The current technique can be divided into two categories: Euler-based methods and Lagrangian-based methods. [0003] Euler-based methods do not distinguish motion from stationary regions, and ...

AI Technical Summary

Problems solved by technology

This not only requires expensive optical flow calculation time, but also needs to apply motion segmentation technology and texture filling technology in each frame of the video. For occlusion boundaries and complex motion areas, human intervention is required. These processes greatly increase the algorithm. Complexity

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