Fish-Eye Lenses-Based Camera Calibration and Panoramic Image Stitching

Abstract

Fish-eye lenses are common in several computer vision applications, such as four-camera surround view driver assistance, where a very wide angle (e.g., 180 degrees) of view is available. Nevertheless, their applicability is usually limited by the lack of an accurate and easy-to-use calibration procedure. In this paper, we present a camera calibration method for fish-eye lenses and a panoramic image stitching framework for calibrated surround images. To achieve the calibration of fish-eye captured images, it only requires to observe a reference planar pattern (e.g., chessboard), followed by offline estimating extrinsic and intrinsic parameters and save the related parameters. Each fish-eye distorted image can then be efficiently online corrected. Then, each calibrated image is transformed to its top-down view (or bird's-eye view) via the perspective transformation based on the estimated homography matrix. As a result, these surround bird'seye view images can be stitched to generate the final panoramic image. It is expected that the proposed framework would be applicable to AVM (around view monitoring) system or ADAS (advanced driver assistance system) of vehicles in the future.