Camera calibration is a crucial step in many computer vision applications that require 3D information about the scene. The process involves estimating intrinsic and extrinsic parameters from a set of captured images. The main challenge is to obtain accurate corner position information from the captured patterns. The conventional method uses only one checkerboard pattern, which suffers from uneven noise distribution. To solve this problem, a calibration method based on complementary patterns is proposed, which reduces the influence of image noise and enhances the calibration accuracy. Experimental results show that the proposed method achieves a 27% reduction in the Root Mean Square of the re-projection error compared with the non-complementary pattern.