Motion Boundary-Guided Optical Flow Filtering Method Based on Collaborative Deep Neural Networks

Abstract

The invention discloses an optical flow filtering method guided by a motion boundary based on a collaborative deep neural network, and constructs an optical flow filtering data set guided by a motion boundary and a collaborative deep neural network. The input is the initial optical flow estimation result and the motion boundary, and the output is the filtered Optical flow estimation results, including the initial optical flow feature extraction subnetwork, motion boundary feature extraction subnetwork, optical flow filtering first subnetwork and optical flow filtering second subnetwork, use the training set to train the collaborative deep neural network, and use the training to complete The collaborative deep neural network filters the initial optical flow estimation results to quickly generate higher-precision optical flow estimation results. The method of the present invention uses a cooperative deep neural network to automatically learn the optical flow filtering process guided by the motion boundary, accurately simulates the complex functional relationship from the input variable to the output optical flow filtering result, and avoids the introduction of new errors by irrelevant edge information other than the motion boundary , to improve the efficiency and accuracy of optical flow filtering.

Description

technical field [0001] The present invention relates to image processing and motion estimation technology, and specifically refers to an optical flow filtering method guided by a motion boundary based on a collaborative deep neural network. Background technique [0002] Optical flow is the two-dimensional instantaneous velocity vector field of all pixels in a video image. As one of the core issues in the field of computer vision, optical flow estimation is the basis of image processing and motion estimation. It has a very wide range of applications in object detection, object recognition, object tracking, object segmentation, video denoising, and video super-resolution. application. The motion boundary is the discontinuous boundary of the optical flow, which divides the optical flow into several regions, and the optical flow value inside each region satisfies the smoothness characteristic. Using the motion boundary to guide the initial optical flow to filter can filter out...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the above problems, the present invention aims to provide an optical flow filtering method guided by the motion boundary of a cooperative deep neural network, which uses a large number of sample data to automatically learn the structural information in the motion boundary, and guides the filtering optimization of the optical flow to solve the current problem. There are problems such as inaccurate modeling of optical flow filtering method and introduction of new errors

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