Camera self-calibration method based on GA-PSO algorithm

Author

Li, Jing ; Yang, Yimin ; Fu, Genping

Author_Institution

Sch. of Autom., Guangdong Univ. of Technol., Guangzhou, China

fYear

2011

fDate

15-17 Sept. 2011

Firstpage

149

Lastpage

152

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.

Keywords

calibration; cameras; computer vision; genetic algorithms; image sensors; particle swarm optimisation; singular value decomposition; GA-PSO algorithm; Kruppa equation; SVD; active vision system; camera self-calibration method; fundamental matrix; genetic algorithm; optimized cost function; particle swarm optimization; single optimization methods; Accuracy; Calibration; Cameras; Cost function; Equations; Genetic algorithms; Mathematical model; Camera self-calibration; GA-PSO; Kruppa equation; genetic algorithm; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-61284-203-5

Type

conf

DOI

10.1109/CCIS.2011.6045050

Filename

6045050