Camera Self Calibration with Varying Parameters by an Unknown Three Dimensional Scene Using the Improved Genetic Algorithm

Abstract

In this paper we present a technique of camera self-calibration using the improved genetic algorithm based on the Kruppa equations which will be developed in the case where the cameras are characterized by the varying intrinsic parameters. A real coding of genetic algorithm is used to solve this problem. The solutions of the intrinsic parameters of various cameras are encoded in a vector of real values. New genetic operators are used to obtain the solutions of the next generation. An optimal estimate of the intrinsic cameras parameters is obtained by minimizing the cost function by using a modified genetic algorithm. Compared with traditional optimization methods, the camera self-calibration by this approach can avoid being trapped in a local minimum, and converges quickly toward the optimal solution without initial estimate of the cameras parameters. Our study is performed on synthetic and real data to demonstrate the validity and performance of the presented approach. The results show that the proposed technique is both accurate and robust.