{"title":"基于GA-PSO算法的摄像机自标定方法","authors":"Jing Li, Yi-min Yang, Genping Fu","doi":"10.1109/CCIS.2011.6045050","DOIUrl":null,"url":null,"abstract":"This paper proposes a new algorithm(GA-PSO) by combining genetic algorithm and particle swarm optimization to improve the accuracy of camera self-calibration based on the Kruppa equation. Firstly, the simplified Kruppa equations based on the SVD of the fundamental matrix is converted into the optimized cost function. Secondly, the minimum value of the optimized cost function is calculated by GA-PSO. Finally, the intrinsic parameters of the camera is obtained. The experimental results show that it is accurate, and the accuracy of the proposed method is obviously improved compared with the single optimization methods.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"136 13","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"Camera self-calibration method based on GA-PSO algorithm\",\"authors\":\"Jing Li, Yi-min Yang, Genping Fu\",\"doi\":\"10.1109/CCIS.2011.6045050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new algorithm(GA-PSO) by combining genetic algorithm and particle swarm optimization to improve the accuracy of camera self-calibration based on the Kruppa equation. Firstly, the simplified Kruppa equations based on the SVD of the fundamental matrix is converted into the optimized cost function. Secondly, the minimum value of the optimized cost function is calculated by GA-PSO. Finally, the intrinsic parameters of the camera is obtained. The experimental results show that it is accurate, and the accuracy of the proposed method is obviously improved compared with the single optimization methods.\",\"PeriodicalId\":128504,\"journal\":{\"name\":\"2011 IEEE International Conference on Cloud Computing and Intelligence Systems\",\"volume\":\"136 13\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Cloud Computing and Intelligence Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCIS.2011.6045050\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIS.2011.6045050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Camera self-calibration method based on GA-PSO algorithm
This paper proposes a new algorithm(GA-PSO) by combining genetic algorithm and particle swarm optimization to improve the accuracy of camera self-calibration based on the Kruppa equation. Firstly, the simplified Kruppa equations based on the SVD of the fundamental matrix is converted into the optimized cost function. Secondly, the minimum value of the optimized cost function is calculated by GA-PSO. Finally, the intrinsic parameters of the camera is obtained. The experimental results show that it is accurate, and the accuracy of the proposed method is obviously improved compared with the single optimization methods.