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The camera self-calibration technology is not limited by the calibration plate and camera motion trajectory, and the calibration process is simple and applicable. Because the traditional genetic algorithm is prone to premature convergence, stagnation and easy to fall into local optimum in the camera self-calibration parameter optimization process, a method to improve the genetic algorithm is proposed. Firstly, the genetic algorithm is improved by combining the elite retention strategy and the random league selection algorithm as the method of initializing the population, improving the roulette selection method, adopting the adaptive hybridization probability and the mutation probability method; then, transforming the simplified Kruppa equation defined by Hartley For the objective function, the improved genetic algorithm is used to search for the optimal value of the objective function. Finally, the experimental results show that the method can better alleviate the premature convergence and stagnation and improve the accuracy.