基于GA-PSO算法的摄像机自标定方法

Jing Li, Yi-min Yang, Genping Fu
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引用次数: 28

摘要

为了提高基于Kruppa方程的摄像机自标定精度,提出了一种将遗传算法与粒子群优化相结合的GA-PSO算法。首先,将基于基本矩阵奇异值分解的简化Kruppa方程转化为优化后的代价函数;其次,利用GA-PSO算法求出优化后的代价函数的最小值;最后,得到了摄像机的固有参数。实验结果表明,该方法是准确的,与单一的优化方法相比,该方法的精度有明显提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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