{"title":"Efficient registration algorithm for UAV image sequence","authors":"Baojie Fan, Yingkui Du, Yandong Tang","doi":"10.1109/ICINFA.2011.5948972","DOIUrl":null,"url":null,"abstract":"This paper presents a fast and efficient image registration algorithm for UAV image sequence. The proposed algorithm consists of three main steps: feature extraction, feature point tracking, and homography matrix estimation. According to the comparison with different feature points, we choose the KLT feature and track them during the consecutive images. With the correct tracked feature points, the Total Least Squares (TLS) method is used to estimate homography matrix between two consecutive images, and then the multi-view constraint is used to refine the result and reduce the accumulative error in the image sequence. This method is robust and with less iteration times. Experiments on different image sequences indicate that our method has satisfactory image registration results with the average time 0.3s.","PeriodicalId":299418,"journal":{"name":"2011 IEEE International Conference on Information and Automation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Information and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2011.5948972","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a fast and efficient image registration algorithm for UAV image sequence. The proposed algorithm consists of three main steps: feature extraction, feature point tracking, and homography matrix estimation. According to the comparison with different feature points, we choose the KLT feature and track them during the consecutive images. With the correct tracked feature points, the Total Least Squares (TLS) method is used to estimate homography matrix between two consecutive images, and then the multi-view constraint is used to refine the result and reduce the accumulative error in the image sequence. This method is robust and with less iteration times. Experiments on different image sequences indicate that our method has satisfactory image registration results with the average time 0.3s.