Calibration of camera internal parameters based on grey wolf optimization improved by levy flight and mutation

Traditional calibration technology has been widely used in measurement and monitoring; however, there are limitations of poor calibration accuracy, which can not meet the accuracy requirements in some scenarios. About this problem, we proposed a grey wolf optimization algorithm based on levy flight and mutation mechanism to solve camera internal parameters in this paper. The algorithm is based on the actual nonlinear model, which takes the minimum average value of reprojection error as the objective function. The grey wolf position is randomly generated within a given range. Then, the grey wolf optimization algorithm based on levy flight and mutation mechanism is used to iteratively calculate the optimal position, which is the internal parameters of cameras. The two groups of experimental data were performed to verify the algorithm. The result shows better effectiveness and calibration accuracy of the proposed algorithm compared with other optimization methods.