Camera Calibration Based on Particle Swarm Optimization

Xiaona Song, Bo Yang, Zhiquan Feng, Ting-xin Xu, Deliang Zhu, Yan Jiang
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引用次数: 16

Abstract

A novel camera calibration approach based on Particle Swarm Optimization(PSO) is put forward in this paper. Firstly, we designed 35 sample points on the calibration box; Secondly, the 3D point and their corresponding 2D image coordi- nate of these sample points were obtained; In this approach, PSO algorithm was adopted to obtain the camera intrinsic parameters. Among the 35 sample points, 30 points were used for training, and other 5 points were mainly used to evaluate the effectiveness of camera calibration. The techniques presented in this paper have been implemented and tested with both synthetic and real data. Our experimental results show that the method can obtain satisfying calibration accuracy.
基于粒子群优化的摄像机标定
提出了一种基于粒子群算法的摄像机标定方法。首先,在标定盒上设计了35个采样点;其次,得到这些样本点的三维点及其对应的二维图像坐标;该方法采用粒子群算法获取摄像机的内在参数。在35个样本点中,30个点用于训练,另外5个点主要用于评估摄像机标定的有效性。本文提出的技术已经在综合数据和实际数据上进行了实现和测试。实验结果表明,该方法能获得满意的标定精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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