Huang Ying-qing, Jiang Xiao-yu, Liu Zhong-xuan, Du Deng-chong, Yao Jun
{"title":"Application of adaptive α-β filtering algorithm to electronic image stabilization","authors":"Huang Ying-qing, Jiang Xiao-yu, Liu Zhong-xuan, Du Deng-chong, Yao Jun","doi":"10.1109/MEC.2011.6025466","DOIUrl":null,"url":null,"abstract":"In the present paper, an adaptive α-β filtering algorithm is adapted for the motion compensation of EIS(electronic image stabilization). It is characterized of less computation, and easy to be realized in real-time. Inter-frame global motion vectors were predicted by feature corners matching based motion estimation. The global camera motion was defined in terms of constant velocity motion models, and adaptive α-β filtering was employed to facilitate smooth operation. Experimental results for many video sequences show that the method can eliminate high frequency dithering, remain the meaningful motion of camera and realize real-time image stabilization satisfactorily. The present algorithm is more robust and higher veracity comparing with other algorithms.","PeriodicalId":386083,"journal":{"name":"2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MEC.2011.6025466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the present paper, an adaptive α-β filtering algorithm is adapted for the motion compensation of EIS(electronic image stabilization). It is characterized of less computation, and easy to be realized in real-time. Inter-frame global motion vectors were predicted by feature corners matching based motion estimation. The global camera motion was defined in terms of constant velocity motion models, and adaptive α-β filtering was employed to facilitate smooth operation. Experimental results for many video sequences show that the method can eliminate high frequency dithering, remain the meaningful motion of camera and realize real-time image stabilization satisfactorily. The present algorithm is more robust and higher veracity comparing with other algorithms.