Neuro-Calibration of a Camera Using Particle Swarm Optimization

In this paper, a particle swarm optimization (PSO) based camera calibration approach is presented to determine the external and internal calibration parameters from the knowledge of a given set of points in object space. First, the image formation model for a pinhole camera is formulated in terms of a feed-forward neural network (NN) and then this neural network is trained using particle swarm optimization. The effect of noise and number of control points are studied in the estimation of calibration parameters. Results from our extensive study are presented to demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness.