Research On Homography Transformation Estimation Based On Convolutional Neural Network And Its Application

Advances in science and technology promote the continuous development of computer vision technology.The homography transformation estimation of the image is particularly important as the basic problem of computer vision.It has a wide range of applications in image alignment and stitching,camera pose estimation and visual navigation.In view of the lack of datasets and insufficient use of features in the current homography transformation estimation algorithm based on convolutional neural network,in-depth research is carried out.The main work is as follows:(1)Built a large homography transformation dataset in real-world scene.Deep learning algorithm needs a lot of data with accurate annotation information,and in the current homography transformation estimation algorithm based on convolutional neural network,most of them use synthetic method to generate the required data,which can not simulate the complex and changeable natural environment.Therefore,this thesis constructs a dataset for training and evaluating the homography transform estimation algorithm.The dataset contains 50 different scenes,and each scene contains different weather or day-and-night conditions to increase the complexity of the dataset.In addition,in order to reduce the huge cost of data annotation,an automatic annotation tool is designed to annotate the data.And make manual secondary correction and fine-tuning to ensure the rationality and accuracy of the annotation results.(2)Proposed a homography transformation estimation algorithm based on convolutional neural network and applied it to the planar object tracking problem.Firstly,analyzed the advantages and disadvantages of the existing homography transformation estimation algorithms based on convolutional neural network,and proposed a homography transformation estimation algorithm based on convolutional neural network.In the model of thesis,in order to highlight the image convolution features that contribute to the estimation of image homography transformation,weighted the extracted image convolution features,and matched the convolution features of the two images to simulate traditional machine vision algorithms in the process of feature matching,so as to estimate more accurate homography transformation parameters.Compared with the existing similar algorithms,the algorithm in this thesis can capture the image features with practical significance,and can better associate the image convolution features,and improve the overall performance of the algorithm.Secondly,the homography transformation estimation algorithm based on CNN proposed in this thesis is applied to planar object tracking,used homography transformation parameters between two frames to update the tracking bounding box.The results of the experiments show that the homography transformation estimation algorithm based on convolutional neural network proposed in this thesis is more accurate than other similar algorithms,which verifies the effectiveness of the algorithm.