We propose a fisheye camera calibration algorithm, in which the center of the distortion and equivalent focal length of the fisheye camera are obtained by the contour constraint, and the distortion parameters are calculated by linear constraint and Levenberg-Marquardt (LM) optimization algorithm. According to the obtained camera parameters, the fisheye image can be corrected by cubic-box-mapping-method (CBMM). The result shows that for checkerboard images the calibration algorithm demonstrates a high-accuracy of about 0.6727 pixels in the average reprojection root-mean-square error (RMSE). Based on the calibration algorithm, the ream-time fisheye video correction is implemented on an android smart-phone. In the implementation, a direct mapping method is used to simplify the cubic box mapping method, and a heterogeneous architecture with a graphics processing unit (GPU) is built to accelerate the algorithm. Lastly, a fisheye video stream of 1280×1280 resolution is timely corrected on the smart-phone with a speed of 25 frames/sec.

我们提出了一种鱼眼镜头标定算法，该算法通过轮廓约束获得鱼眼镜头的畸变中心和等效焦距，并通过线性约束和Levenberg-Marquardt（LM）优化算法来计算畸变参数。根据获得的相机参数，可以通过立方盒映射方法（CBMM）校正鱼眼图像。结果表明，对于棋盘图像，校准算法在平均重投影均方根误差（RMSE）中显示出约0.6727像素的高精度。基于校准算法，在安卓智能手机上实现了扩眼鱼眼视频校正。在实现中，使用直接映射方法来简化立方盒映射方法，并且构建了带有图形处理单元（GPU）的异构体系结构来加速算法。最后，在智能手机上以25帧/秒的速度及时校正1280×1280分辨率的鱼眼视频流。