Automatic checkerboard detection for camera calibration using self-correlation

The checkerboard is a frequently-used pattern in camera calibration, an essential process to get intrinsic parameters for more accurate information from images. An automatic checkerboard detection method that can detect multiple checkerboards in a single image is proposed in this paper. It contains a corner extraction approach using self-correlation and a structure recovery solution using constraints related to adjacent corners and checkerboard block edges. The method utilizes the central symmetric feature of the checkerboard crossings as well as the spatial relationship of neighboring checkerboard corners and the grayscale distribution of their neighboring pixels. Five public datasets are used in the experiments to evaluate the method. Results show high detection rates and a short average runtime of the proposed method. In addition, the camera calibration accuracy also presents the effectiveness of the proposed detection method with re-projected pixel errors smaller than 0.5 pixels.