A new method of camera self-calibration with varying intrinsic parameters using an improved genetic algorithm

In this paper, we present a new method of camera self-calibration with varying intrinsic parameters by an improved genetic algorithm. Firstly, the simplified Kruppa equation (the case of varying intrinsic parameters) defined by Hartley is translated into the optimized cost function. Secondly, the minimization of the cost function is calculated by an optimized modified genetic algorithm. Finally, the intrinsic parameters of the camera are obtained. Comparing to traditional optimization methods, the camera self-calibration with varying intrinsic parameters by this approach can avoid being trapped in a local minimum and converge quickly to the optimal solution without initial estimates of the camera 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.